阅读说明：本技术 用于自动驾驶车辆在高速公路上的自动驾驶的无地图无定位的沿车道行驶方法 (Map-free and location-free lane-following driving method for the automatic driving of an autonomous vehicle on a highway ) 是由 朱帆 孔旗 潘余昌 蒋菲怡 许昕 付骁鑫 夏中谱 赵春明 张亮亮 朱伟铖 庄立 于 2018-04-18 设计创作，主要内容包括：代替使用地图数据,使用相对坐标系来帮助在一些驾驶场景下感知ADV周围的驾驶环境。所述驾驶场景的一种是在高速公路上驾驶。通常,高速公路具有较少的十字路口和出口。基于相对车道配置和相对障碍物信息在不使用地图数据的情况下使用相对坐标系来控制ADV简单地沿车道行驶并避免与在道路内发现的任意障碍物的潜在碰撞。一旦相对车道配置和障碍物信息被确定,则可执行常规的路径和速度规划和优化,以生成驾驶ADV的轨迹。这种定位系统被称为基于相对坐标系的相对定位系统。(Instead of using map data, a relative coordinate system is used to help perceive the driving environment around the ADV in some driving scenarios. One such driving scenario is driving on a highway. Typically, a highway has fewer intersections and exits. Controlling the ADV using a relative coordinate system based on relative lane configuration and relative obstacle information without using map data simply travels along the lane and avoids potential collisions with any obstacles found within the road. Once the relative lane configuration and obstacle information are determined, conventional path and speed planning and optimization may be performed to generate a trajectory for driving the ADV. Such a positioning system is referred to as a relative positioning system based on a relative coordinate system.)

1. A computer-implemented method for generating a trajectory to operate an autonomous vehicle to travel along a lane, the method comprising:

determining a driving environment around an autonomous vehicle (ADV) driving on a lane based on sensor data obtained from a plurality of sensors;

determining a lane configuration of the lane relative to a current location of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane;

determining obstacle information for an obstacle relative to the ADV, including a relative position of the obstacle relative to the current position of the ADV;

generating a local view frame based on the lane configuration and the obstacle information, the local view frame describing the lane configuration and the obstacle from a perspective of the ADV without map information; and

generating a trajectory for the ADV to travel along the lane for a next driving cycle based on the local view frame.

2. The method of claim 1, further comprising: prior to determining the lane configuration of the lane, determining that the ADV is driving on an expressway based on perceptual data of a perceived driving environment, wherein the lane configuration is determined in response to determining that the ADV is driving on an expressway.

3. The method of claim 1, further comprising: for each of a plurality of driving cycles,

repeatedly performing determining the driving environment, determining the lane configuration, determining the obstacle information, and generating the local view frames for respective driving cycles; and

storing the local view frame in a persistent storage, wherein the local view frame associated with the driving cycle is used to create the trajectory.

4. The method of claim 1, wherein the lane is one of a plurality of lanes of a road driven by the ADV, wherein generating a trajectory for driving the ADV comprises:

generating a plurality of local view frames based on the lane configuration and the obstacle information, each local view frame corresponding to one of the plurality of lanes;

generating a plurality of paths, each path corresponding to one of the local-view frames; and

selecting one of the plurality of paths based on a path cost associated with each path using a predetermined cost function, wherein the selected path is used to generate the trajectory.

5. The method of claim 1, wherein determining a lane configuration of the lane relative to the ADV comprises:

measuring a first distance between a current position of the ADV and a first edge of the lane based on a first image of the first edge of the lane;

measuring a second distance between the current position of the ADV and a second edge of the lane based on a second image of the second edge of the lane; and

calculating a width of the lane based on the first distance and the second distance.

6. The method of claim 1, further comprising generating a reference line associated with the lane according to a lane width of the lane based on the lane configuration without the map data, wherein the reference line represents a centerline within the lane.

7. The method of claim 6, further comprising generating a distance traveled-lateral offset (SL) map based on the lane configuration and the obstacle information, wherein the SL map includes information describing a lateral position of the obstacle relative to the reference line and a vertical position relative to a current position of the ADV, the SL map being used to optimize a shape of the trajectory.

8. The method of claim 6, further comprising generating a distance traveled versus time (ST) map based on the lane configuration and the obstacle information, wherein the ST map includes information describing a relative position of the obstacle with respect to a reference line at different points in time, the ST map being used to optimize a speed of the ADV along the trajectory at the different points in time.

9. The method of claim 1, wherein the current position of the ADV is measured based on a position of a center point of a rear axis of the ADV.

10. A non-transitory machine-readable medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform operations comprising:

determining a driving environment around an ADV driving on a lane, including sensing an obstacle, based on sensor data obtained from a plurality of sensors of an autonomous vehicle ADV;

determining a lane configuration of the lane relative to a current location of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane;

determining obstacle information for the obstacle relative to the ADV, including a relative position of the obstacle relative to the current position of the ADV;

generating a local view frame based on the lane configuration and obstacle information of the obstacle, the local view frame describing the lane configuration and the obstacle from a perspective of the ADV without map information; and

generating a trajectory for the ADV to travel along the lane for a next driving cycle based on the local view frame.

11. The machine-readable medium of claim 10, wherein the operations further comprise: prior to determining the lane configuration of the lane, determining that the ADV is driving on an expressway based on perceptual data of a perceived driving environment, wherein the lane configuration is determined in response to determining that the ADV is driving on an expressway.

12. The machine-readable medium of claim 10, wherein the operations further comprise: for each of a plurality of driving cycles,

repeatedly performing determining the driving environment, determining the lane configuration, determining the obstacle information, and generating the local view frames for respective driving cycles; and

storing the local view frame in a persistent storage, wherein the local view frame associated with the driving cycle is used to create the trajectory.

13. The machine-readable medium of claim 10, wherein the lane is one of a plurality of lanes of a roadway driven by the ADV, wherein generating a trajectory to drive the ADV comprises:

generating a plurality of local view frames based on the lane configuration and the obstacle information, each local view frame corresponding to one of the plurality of lanes;

generating a plurality of paths, each path corresponding to one of the local-view frames; and

selecting one of the plurality of paths based on a path cost associated with each path using a predetermined cost function, wherein the selected path is used to generate the trajectory.

14. The machine-readable medium of claim 10, wherein determining a lane configuration of the lane relative to the ADV comprises:

measuring a first distance between a current position of the ADV and a first edge of the lane based on a first image of the first edge of the lane;

measuring a second distance between the current position of the ADV and a second edge of the lane based on a second image of the second edge of the lane; and

calculating a width of the lane based on the first distance and the second distance.

15. The machine-readable medium of claim 10, wherein the operations further comprise generating, without the map data, a reference line associated with the lane according to a lane width of the lane based on the lane configuration, wherein the reference line represents a centerline within the lane.

16. The machine-readable medium of claim 10, wherein the operations further comprise generating a distance traveled-lateral offset (SL) map based on the lane configuration and the obstacle information, wherein the SL map includes information describing a lateral position of the obstacle relative to the reference line and a vertical position relative to a current position of the ADV, the SL map being used to optimize a shape of the trajectory.

17. The machine-readable medium of claim 10, wherein the operations further comprise driving a distance-time, ST, map based on the lane configuration and the obstacle information, wherein the ST map comprises information describing a relative position of the obstacle with respect to a reference line at different points in time, the ST map being used to optimize a speed of the ADV at the different points in time along the trajectory.

18. The machine-readable medium of claim 10, wherein the current position of the ADV is measured based on a position of a center point of a rear axis of the ADV.

19. A data processing system comprising:

a processor; and

a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

determining a driving environment around an ADV driving on a lane, including sensing an obstacle, based on sensor data obtained from a plurality of sensors of an autonomous vehicle ADV;

determining a lane configuration of the lane relative to a current location of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane;

determining obstacle information for the obstacle relative to the ADV, including a relative position of the obstacle relative to the current position of the ADV;

generating a local view frame based on the lane configuration and the obstacle information, the local view frame describing the lane configuration and the obstacle from a perspective of the ADV without map information; and

generating a trajectory for the ADV to travel the lane for a next driving cycle based on the local view frame.

20. The system of claim 19, wherein the operations further comprise: prior to determining the lane configuration of the lane, determining that the ADV is driving on an expressway based on perceptual data of a perceived driving environment, wherein the lane configuration is determined in response to determining that the ADV is driving on an expressway.

Technical Field

Embodiments of the present disclosure generally relate to operating an autonomous vehicle. More particularly, embodiments of the present disclosure relate to methods for autonomous, map-less, location-free, highway driving.

Background

Vehicles operating in an autonomous driving mode (e.g., unmanned) may relieve occupants, particularly the driver, from some driving-related duties. When operating in an autonomous driving mode, the vehicle may be navigated to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.

Motion planning and control are key operations for autonomous driving. Generally, in order to drive an autonomous vehicle (ADV) in a lane of a road, the lane configuration of the lane has been determined and obstacles within or near the lane must be identified. Conventional autopilot systems utilize map data to determine lane configurations (e.g., the shape of the lane and the lane width), which are referred to herein as part of the positioning process. Such positioning systems are known as global or absolute positioning systems, in which the lane configuration is determined on the basis of global or absolute coordinates. However, such map data may sometimes be unavailable. For example, if the lane or road is new and the map data is not updated in time. In other cases, the map data may not be updated due to network traffic congestion.

Disclosure of Invention

Embodiments of the present disclosure provide a computer-implemented method, a non-transitory machine-readable medium, and a data processing system for generating a trajectory to operate an autonomous vehicle to travel along a lane.

In one aspect of the disclosure, a computer-implemented method for generating a trajectory to operate an autonomous vehicle to travel along a lane includes: determining a driving environment around an autonomous vehicle (ADV) driven on a lane based on sensor data obtained from a plurality of sensors; determining a lane configuration of the lane relative to a current position of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane; determining obstacle information for the obstacle relative to the ADV, including a relative position of the obstacle relative to a current position of the ADV; generating a local view frame based on the lane configuration and the obstacle information, the local view frame describing the lane configuration and the obstacle from the perspective of the ADV without map information; and generating a trajectory for the ADV to travel along the lane in a next driving cycle based on the local view frame.

In another aspect of the disclosure, a non-transitory machine-readable medium stores instructions that, when executed by a processor, cause the processor to: determining a driving environment around an autonomous vehicle (ADV) driving on a lane based on sensor data obtained from a plurality of sensors of the ADV, including sensing an obstacle; determining a lane configuration of the lane relative to a current position of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane; determining obstacle information for the obstacle relative to the ADV, including a relative position of the obstacle relative to a current position of the ADV; generating a local view frame based on the lane configuration and the obstacle information of the obstacle, the local view frame describing the lane configuration and the obstacle from the viewpoint of the ADV without the map information; and generating a trajectory for the ADV to travel along the lane in a next driving cycle based on the local view frame.

In yet another aspect of the disclosure, a data processing system includes a processor and a memory coupled to the processor to store instructions that, when executed by the processor, cause the processor to: determining a driving environment around an autonomous vehicle (ADV) driving on a lane based on sensor data obtained from a plurality of sensors of the ADV, including sensing an obstacle; determining a lane configuration of the lane relative to a current position of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane; determining obstacle information for the obstacle relative to the ADV, including a relative position of the obstacle relative to a current position of the ADV; generating a local view frame based on the lane configuration and the obstacle information, the local view frame describing the lane configuration and the obstacle from the perspective of the ADV without map information; and generating a trajectory for the ADV to travel along the lane in a next driving cycle based on the local view frame.

Drawings

Embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements.

FIG. 1 is a block diagram illustrating a networked system according to one embodiment.

Fig. 2 is a block diagram showing an example of an autonomous vehicle according to the present embodiment.

Fig. 3A-3B are block diagrams illustrating an example of a perception and planning system for use with an autonomous vehicle, according to one embodiment.

FIG. 4 is a block diagram illustrating an example of a relative coordinate system according to one embodiment.

Fig. 5 is a block diagram illustrating a process of determining obstacle and lane configuration information according to one embodiment.

FIG. 6 is a process flow diagram illustrating stages in a process for operating an autonomous vehicle, according to one embodiment.

Fig. 7 is a diagram illustrating an example of a traveled distance-lateral offset map according to an embodiment.

Fig. 8 is a diagram illustrating an example of a travel distance-time map according to an embodiment.

FIG. 9 is a flow chart illustrating a process of operating an autonomous vehicle according to one embodiment.

FIG. 10 is a block diagram illustrating a data processing system in accordance with one embodiment.

Detailed Description

Various embodiments and aspects of the disclosure will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the disclosure and are not to be construed as limiting the disclosure. Numerous specific details are described to provide a thorough understanding of various embodiments of the present disclosure. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present disclosure.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

According to some embodiments, instead of using map data, a relative coordinate system is used to help perceive and/or locate the driving environment around the ADV in some driving situations. One such driving situation is driving on a highway. Typically, a highway has fewer intersections and exits. Controlling the ADV to simply travel along the lane and avoid sending potential collisions with any obstacles found within the roadway using the relative coordinate system based on the relative lane configuration and relative obstacle information relative to the ADV without having to use map data. Once the relative lane configuration and obstacle information are determined, conventional path and speed planning and optimization may be performed as usual to generate a trajectory to drive the ADV. Such a positioning system is referred to as a relative positioning system based on a relative coordinate system.

According to one embodiment, sensing a driving environment around an ADV driving on a lane based on sensor data obtained from various sensors mounted on the ADV includes sensing at least one obstacle. Determining a lane configuration of the lane with respect to a current position of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane. Obstacle information describing at least one obstacle is determined based on the perception data. The obstacle information includes relative position information of the obstacle (e.g., located in a local driving environment, such as within one or more lanes) relative to a current position of the ADV. The local view frame is generated based on the lane configuration and the obstacle information. The local view frame includes information describing lane configuration and obstacle information from the perspective of the ADV without map information. A trajectory is generated based on the local view frame that controls the ADV to travel along the lane in a next driving cycle. The trajectory is arranged in relative coordinates from the ADV perspective.

In one embodiment, the above process is performed iteratively for each driving cycle, with the local view frames generated for each driving cycle being stored and maintained in a database of a persistent storage device (e.g., hard disk) for tracking ADV and obstacle movement relative to each other, as well as lane curvature and shape, and the like. Thus, the driving environment of an ADV may be represented by a sequence of local view frames at different points in time. In one embodiment, a trajectory for driving an ADV along a lane is generated using a local view frame without using map data in response to determining that the lane is part of a highway.

If the highway includes multiple lanes, a local view frame is generated for each lane and a track is generated for each lane based on the respective local view frame. One of the trajectories is selected as a final trajectory to drive the ADV based on the costs associated with the trajectories. The trajectory cost may be calculated using one or more cost functions based on various cost factors, such as the potential risk of collision with an obstacle, the comfort of the passenger, and so forth. In one embodiment, in determining a lane configuration of a lane, images acquired by a sensor of an ADV are analyzed to measure a first distance between the ADV and a first edge of the lane and to measure a second distance between the ADV and a second edge of the lane. The lane width of the lane is calculated as part of the lane configuration using the first distance and the second distance. Based on the lane width, a center point (also referred to as a midpoint or reference point) of the lane can be calculated. The reference points of the local view frame sequence may be used to derive reference lines for the lanes.

In one embodiment, a distance-to-lateral offset (SL) map is generated to map the relative position (e.g., x, y coordinates) of the obstacle with respect to the reference line of the lane and the current position of the ADV based on the reference line of the lane and the current position of the ADV. For example, coordinate x may represent a longitudinal distance between the obstacle and the current position of the ADV, while coordinate y may represent a lateral distance between the obstacle and a reference line of the lane. SL maps may be used to plan and optimize the shape of the trajectory to navigate between obstacles without collision while providing maximum comfort to the passengers. According to another embodiment, a distance-time-of-travel (ST) map is generated to map the relative position of the obstacle with respect to the current position of the ADV at different points in time in the future based on the reference line of the lane and the current position of the ADV. Thus, the coordinate y represents the distance between the obstacle and the ADV, while the coordinate x represents time. The ST map may be used to optimize the speed of the ADV at different points in time to navigate through the obstacle without collision while providing maximum comfort to the occupant.

Fig. 1 is a block diagram illustrating an autonomous vehicle network configuration according to one embodiment of the present disclosure. Referring to fig. 1, a network configuration 100 includes an autonomous vehicle 101 that may be communicatively coupled to one or more servers 103-104 through a network 102. Although one autonomous vehicle is shown, multiple autonomous vehicles may be coupled to each other and/or to servers 103-104 through network 102. The network 102 may be any type of network, such as a wired or wireless Local Area Network (LAN), a Wide Area Network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof. The servers 103-104 may be any type of server or cluster of servers, such as a network or cloud server, an application server, a backend server, or a combination thereof. The servers 103 to 104 may be data analysis servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc.

Autonomous vehicles refer to vehicles that may be configured to be in an autonomous driving mode in which the vehicle navigates through the environment with little or no input from the driver. Such autonomous vehicles may include a sensor system having one or more sensors configured to detect information related to the operating environment of the vehicle. The vehicle and its associated controller use the detected information to navigate through the environment. Autonomous vehicle 101 may operate in a manual mode, in a fully autonomous mode, or in a partially autonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limited to, a perception and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, an infotainment system 114, and a sensor system 115. Autonomous vehicle 101 may also include certain common components included in a common vehicle, such as: engines, wheels, steering wheels, transmissions, etc., which may be controlled by the vehicle control system 111 and/or the sensory and programming system 110 using a variety of communication signals and/or commands, such as, for example, acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc.

The components 110-115 may be communicatively coupled to each other via an interconnect, bus, network, or combination thereof. For example, the components 110-115 may be communicatively coupled to one another via a Controller Area Network (CAN) bus. The CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host. It is a message-based protocol originally designed for multiplexed electrical wiring within automobiles, but is also used in many other environments.

Referring now to fig. 2, in one embodiment, the sensor system 115 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU)213, a radar unit 214, and a light detection and ranging (LIDAR) unit 215. The GPS system 212 may include a transceiver operable to provide information regarding the location of the autonomous vehicle. The IMU unit 213 may sense position and orientation changes of the autonomous vehicle based on inertial acceleration. Radar unit 214 may represent a system that utilizes radio signals to sense objects within the local environment of an autonomous vehicle. In some embodiments, in addition to sensing an object, radar unit 214 may additionally sense a speed and/or heading of the object. The LIDAR unit 215 may use a laser to sense objects in the environment in which the autonomous vehicle is located. The LIDAR unit 215 may include one or more laser sources, laser scanners, and one or more detectors, among other system components. The camera 211 may include one or more devices used to capture images of the environment surrounding the autonomous vehicle. The camera 211 may be a still camera and/or a video camera. The camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting platform.

The sensor system 115 may also include other sensors, such as: sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and audio sensors (e.g., microphones). The audio sensor may be configured to collect sound from an environment surrounding the autonomous vehicle. The steering sensor may be configured to sense a steering angle of a steering wheel, wheels of a vehicle, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated into an integrated throttle/brake sensor.

In one embodiment, the vehicle control system 111 includes, but is not limited to, a steering unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a brake unit 203. The steering unit 201 is used to adjust the direction or forward direction of the vehicle. The throttle unit 202 is used to control the speed of the motor or engine, which in turn controls the speed and acceleration of the vehicle. The brake unit 203 decelerates the vehicle by providing friction to decelerate the wheels or tires of the vehicle. It should be noted that the components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Returning to fig. 1, wireless communication system 112 allows communication between autonomous vehicle 101 and external systems such as devices, sensors, other vehicles, and the like. For example, the wireless communication system 112 may be in direct wireless communication with one or more devices, or in wireless communication via a communication network, such as with the servers 103-104 through the network 102. The wireless communication system 112 may use any cellular communication network or Wireless Local Area Network (WLAN), for example, using WiFi, to communicate with another component or system. The wireless communication system 112 may communicate directly with devices (e.g., passenger's mobile device, display device, speaker within the vehicle 101), for example, using infrared links, bluetooth, etc. The user interface system 113 may be part of a peripheral device implemented within the vehicle 101, including, for example, a keypad, a touch screen display device, a microphone, and speakers, among others.

Some or all of the functions of the autonomous vehicle 101 may be controlled or managed by the perception and planning system 110, particularly when operating in an autonomous mode. The awareness and planning system 110 includes the necessary hardware (e.g., processors, memory, storage devices) and software (e.g., operating systems, planning and routing programs) to receive information from the sensor system 115, the control system 111, the wireless communication system 112, and/or the user interface system 113, process the received information, plan a route or path from the origin to the destination, and then drive the vehicle 101 based on the planning and control information. Alternatively, the sensing and planning system 110 may be integrated with the vehicle control system 111.

For example, a user who is a passenger may specify a start location and a destination of a trip, e.g., via a user interface. The perception and planning system 110 obtains trip related data. For example, the sensing and planning system 110 may obtain location and route information from an MPOI server, which may be part of the servers 103-104. The location server provides location services and the MPOI server provides map services and POIs for certain locations. Alternatively, such location and MPOI information may be cached locally in persistent storage of the sensing and planning system 110.

The perception and planning system 110 may also obtain real-time traffic information from a traffic information system or server (TIS) as the autonomous vehicle 101 moves along the route. It should be noted that the servers 103 to 104 may be operated by third party entities. Alternatively, the functionality of the servers 103-104 may be integrated with the perception and planning system 110. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environmental data (e.g., obstacles, objects, nearby vehicles) detected or sensed by sensor system 115, perception and planning system 110 may plan an optimal route and drive vehicle 101, e.g., via control system 111, according to the planned route to safely and efficiently reach a designated destination.

Server 103 may be a data analysis system to perform data analysis services for various clients. In one embodiment, data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistical data 123 from various vehicles (autonomous vehicles or ordinary vehicles driven by a driver). The driving statistics 123 include information indicative of the driving commands issued (e.g., throttle, brake, steering commands) and vehicle responses (e.g., speed, acceleration, deceleration, direction) collected by sensors of the vehicle at different points in time. The driving statistics 123 may also include information describing the driving environment at different points in time, such as a route (including a start location and a destination location), MPOI, road conditions, weather conditions, and the like.

Based on the driving statistics 123, the machine learning engine 122 generates or trains a set of rules, algorithms, and/or predictive models 124 for various purposes. The algorithm 124 includes a relative coordinate algorithm that calculates the relative position of the obstacle with respect to the current position of the ADV without using map data and an algorithm that determines the lane configuration of the lane with respect to the ADV. The algorithm 124 may also include an algorithm that determines whether the ADV is currently driving on the highway to wake up the relative or local coordinate system.

Fig. 3A and 3B are block diagrams illustrating an example of a perception and planning system for use with an autonomous vehicle, according to one embodiment. The system 300 may be implemented as part of the autonomous vehicle 101 of fig. 1, including but not limited to the perception and planning system 110, the control system 111, and the sensor system 115. Referring to fig. 3A-3B, sensing and planning system 110 includes, but is not limited to, a positioning module 301, a sensing module 302, a prediction module 303, a decision module 304, a planning module 305, a control module 306, a routing module 307, and a relative coordinate system 308.

Some or all of the modules 301 to 308 may be implemented in software, hardware, or a combination thereof. For example, the modules may be installed in persistent storage 352, loaded into memory 351, and executed by one or more processors (not shown). It should be noted that some or all of these modules may be communicatively coupled to or integrated with some or all of the modules of the vehicle control system 111 of fig. 2. Some of the modules 301 to 308 may be integrated together into an integrated module.

The location module 301 determines the relative location of the autonomous vehicle (e.g., using the GPS unit 212) and manages any data related to the user's trip or route. The positioning module 301 (also referred to as a map and route module) manages any data related to the user's journey or route. The user may, for example, log in via a user interface and specify a starting location and a destination for the trip. The positioning module 301 communicates with other components of the autonomous vehicle 300, such as map and route information 311, to obtain trip related data. For example, the location module 301 may obtain location and route information from a location server and a map and poi (mpoi) server. The location server provides location services and the MPOI server provides map services and POIs for certain locations and may thus be cached as part of the map and route information 311. The location module 301 may also obtain real-time traffic information from a traffic information system or server as the autonomous vehicle 300 moves along the route.

Based on the sensor data provided by sensor system 115 and the positioning information obtained by positioning module 301, perception module 302 determines a perception of the surrounding environment. The perception information may represent what an average driver would perceive around the vehicle the driver is driving. Perception may include, for example, lane configuration in the form of an object (e.g., a straight lane or a curved lane), a traffic light signal, a relative position of another vehicle, a pedestrian, a building, a crosswalk, or other traffic-related indicia (e.g., a stop sign, a yield sign), and so forth.

The perception module 302 may include a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and/or features in an autonomous vehicle environment. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and/or obstacles, etc. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track objects, and estimate the speed of objects, among other things. The perception module 302 may also detect objects based on other sensor data provided by other sensors, such as radar and/or LIDAR.

For each object, the prediction module 303 predicts how the object will behave under the circumstances. The prediction is performed in view of the set of map/route information 311 and traffic rules 312 based on the perception data that perceives the driving environment at the point in time. For example, if the object is a vehicle in the opposite direction and the current driving environment includes an intersection, the prediction module 303 predicts whether the vehicle is likely to be traveling straight or turning. If the perception data indicates that the intersection has no traffic lights, the prediction module 303 may predict that the vehicle may have to come to a complete stop before entering the intersection. If the perception data indicates that the vehicle is currently located in a left-turn only lane or a right-turn only lane, the prediction module 303 may predict whether the vehicle is more likely to turn left or right.

For each subject, the decision module 304 makes a decision on how to treat the subject. For example, for a particular object (e.g., another vehicle in a crossing route) and metadata describing the object (e.g., speed, direction, turn angle), the decision module 304 decides how to encounter the object (e.g., cut, yield, stop, exceed). The decision module 304 may make such a decision based on a rule set, such as traffic rules or driving rules 312, which may be stored in persistent storage 352.

The routing module 307 is configured to provide one or more routes or paths from a starting point to a destination point. For a given trip from a start location to a destination location (e.g., received from a user), the routing module 307 obtains route and map information 311 and determines all possible routes or paths from the start location to reach the destination location. The routing module 307 may generate a reference line in the form of a topographical map for each of the routes it determines to arrive at the destination location starting from the start location. A reference line refers to an ideal route or path without any interference from other things such as vehicles, obstacles or traffic conditions. That is, if there are no other vehicles, pedestrians, or obstacles on the road, the ADV should correctly or closely follow the reference line. The terrain map is then provided to a decision module 304 and/or a planning module 305. The decision module 304 and/or the planning module 305 examines all possible routes to select and modify one of the best routes, taking into account other data provided by other modules, such as traffic conditions from the positioning module 301, driving environment sensed by the sensing module 302, and traffic conditions predicted by the prediction module 303. Depending on the particular driving environment at the point in time, the actual path or route used to control the ADV may be close to or different from the reference line provided by the routing module 307.

Based on the decisions for each of the perceived objects, the planning module 305 plans a path or route and driving parameters (e.g., distance, speed, and/or turn angle) for the autonomous vehicle based on the reference lines provided by the routing module 307. In other words, for a given object, the decision module 304 decides what to do with the object, and the planning module 305 determines how to do. For example, for a given subject, the decision module 304 may decide to exceed the subject, while the planning module 305 may determine whether to exceed on the left or right side of the subject. Planning and control data is generated by the planning module 305, including information describing how the vehicle 300 will move in the next movement cycle (e.g., the next route/path segment). For example, the planning and control data may instruct the vehicle 300 to move 10 meters at a speed of 30 miles per hour (mph), and then change to the right lane at a speed of 25 mph.

Based on the planning and control data, the control module 306 controls and drives the autonomous vehicle by sending appropriate commands or signals to the vehicle control system 111 according to the route or path defined by the planning and control data. The planning and control data includes sufficient information to drive the vehicle from a first point to a second point of the route or path at different points in time along the route or route using appropriate vehicle settings or driving parameters (e.g., throttle, brake, and steering commands).

In one embodiment, the planning phase is performed in a plurality of planning cycles, also referred to as drive cycles, for example in each 100 millisecond (ms) period. For each planning or driving cycle, one or more control commands will be issued based on the planning and control data. That is, for every 100ms, the planning module 305 plans the next route segment or path segment, including, for example, the target location and the time required for the ADV to reach the target location. Alternatively, the planning module 305 may also specify a particular speed, direction, and/or steering angle, etc. In one embodiment, the planning module 305 plans a route segment or path segment for the next predetermined period of time, e.g., 5 seconds. For each planning cycle, the planning module 305 plans the target location for the current cycle (e.g., the next 5 seconds) based on the planned target location in the previous cycle. The control module 306 then generates one or more control commands (e.g., throttle, brake, steering control commands).

It should be noted that the decision module 304 and the planning module 305 may be integrated as an integrated module. The decision module 304/planning module 305 may include a navigation system or functionality of a navigation system to determine a driving path of an autonomous vehicle. For example, the navigation system may determine a range of speeds and heading directions for enabling the autonomous vehicle to move along the following path: the path substantially avoids perceived obstacles while advancing the autonomous vehicle along a roadway-based path to a final destination. The destination may be set based on user input via the user interface system 113. The navigation system may dynamically update the driving path while the autonomous vehicle is running. The navigation system may combine data from the GPS system and one or more maps to determine a driving path for the autonomous vehicle.

Typically, the positioning module 301 operates in a global (or absolute) coordinate mode. When operating in the global coordinate mode, the positioning module 301 relies on map data, such as map and route information 311, to determine the position of the ADV, the obstacles observed on the road, and the lane configuration of the lanes. Such global positioning processes can be resource and time consuming, sometimes map data is not available or updated in time to include the lane or road the ADV is driving on. The positioning module 301 determines the position of the vehicle and obstacles within the lane based on map data in view of other sensor data (e.g., GPS and IMU data).

According to one embodiment, the relative coordinate system or module 308 (also referred to as a local coordinate system) may be used to determine the location of objects (e.g., ADVs, obstacles) within a lane without the use of map data. The relative coordinate system 308 may be awakened in certain specific environments that do not require the use of map data or that map data is not available. For example, when an ADV is driving on a highway, the relative coordinate system 308 may be used to control the ADV to simply travel along the lane because of the small number of intersections or exits on the highway. In this case, the map data need not be used because the map data may require a large amount of processing resources and time. In other cases, the map data may not be available at all or the map data is not updated to reflect changes in the highway (e.g., a new highway).

In one embodiment, the relative coordinate system 308 determines the relative position of the obstacle with respect to the current position of the ADV and the lane configuration of the lane with respect to the current position of the ADV without having to use map data. This relative positioning process can be performed faster because it does not rely on map data. However, the precise location of the obstacle may not be accurately determined. However, when driving on a highway, ADVs may only need to travel along a lane most of the time, since a highway tends to have a small number of intersections or exits. Thus, waking up the relative coordinate system 308 may be more efficient when driving on a highway.

FIG. 4 is a block diagram illustrating an example of a relative coordinate system according to one embodiment. Reference to

In fig. 4, the relative coordinate system 308 includes a relative obstacle module 411, a relative lane module 412, and a local view module 413. The relative obstacle module 411 is configured to determine obstacle information describing a current position of one or more obstacles detected on the lane or road relative to the ADV. Each obstacle is described in relative coordinates (e.g., x, y coordinates) with respect to the current position of the ADV. The relative lane module 412 is configured to determine a relative lane configuration of the lane relative to a current position of the ADV. The lanes may include a current lane in which the ADV is driving and one or more lanes adjacent to the current lane. The obstacle information and the lane configuration information are collected based on the images collected by the sensors without using map data such as the map data 311. The local view module 413 is configured to generate a local view frame having information describing the driving environment relative to the current position of the ADV or from the perspective of the ADV based on the obstacle information and the lane configuration information. In one embodiment, for each driving cycle, a local view frame is generated, which may be stored and maintained in a permanent storage device as part of local view frame 313.

FIG. 5 is a block diagram illustrating a process of determining obstacle and lane configuration information according to one embodiment. Referring to fig. 5, in this example, assume that the ADV 500 is driving forward in the X-axis on the center lane 521 between the adjacent lanes 522 and 523. Obstacle 501 is located in lane 521, obstacle 502 is located in lane 522, and obstacle 503 is located in lane 523. Based on the images of the lanes 521-523 and the obstacles 501-503, the obstacle module 411 is configured to determine the relative location or position of each of the obstacles 501-503 with respect to a predetermined reference point of the ADV 500. In this example, the center point of the rear axle is selected as reference point 550, although other reference points are also suitable.

In one embodiment, the obstacle module 411 performs image processing on the image of each obstacle to measure the distance between the ADV 500 and the obstacle without using map data. For example, for the obstacle 502, the distance between the obstacle 502 and the reference point 550 is measured using a predetermined image processing algorithm specifically created for this purpose. Relative coordinates (x, y) are determined for the obstacle 502. The coordinate X of the obstacle represents a distance (e.g., ordinate) relative to the reference point 550 on the X-axis, and the coordinate Y of the obstacle represents a distance (e.g., abscissa) relative to the reference point 550 on the Y-axis. Thus, in this example, a positive y-coordinate of the obstacle indicates that the obstacle is located to the left of ADV 500, and a negative y-coordinate of the obstacle indicates that the obstacle is located to the right of ADV 500. Thus, in this example, the y-coordinate of obstacle 502 is positive, while the y-coordinate of obstacle 503 is negative.

Additionally, based on the images of the lanes 521-523, the lane module 412 is configured to identify the edges of each lane and measure the distance between the reference point 550 and the edges of the lanes. For example, for lane 521, lane module 421 determines and measures a first distance between reference point 550 and left lane edge (or left lane boundary) 511 and a second distance between reference point 550 and right lane edge 512. The width of the lane 521 may be calculated using the first distance and the second distance. Assuming that the reference line of the lane is located at the center of the lane, the reference point of the reference line at a specific time point may also be determined. Similarly, the lane width of the lane 522 and the reference point of the reference line may be determined by identifying and measuring the distance between the reference point 550 and the lane boundaries 511 and 513. The lane width of the lane 523 and the reference point of the reference line may be determined by identifying and measuring the distance between the reference point 550 and the lane boundaries 512 and 514.

Based on the relative positions of the obstacles 501 to 503 and the lane configuration (e.g., lane width, reference point) of the lane 521 value 523, a local view frame may be created that contains information describing the relative positions of the obstacles 501 to 503 and the lane configuration at a point in time corresponding to the driving cycle. The local view frame is then stored in a permanent storage. As described above, the automatic driving process is repeatedly performed for each driving cycle. For each driving cycle, a local view frame is created. By comparing local view frames, movement and lane configuration (e.g., width, shape/curvature) of the obstacles 501-503 may be tracked. Thus, the speed and the heading direction of the obstacles 501-503 may also be determined based on the local view frame sequence.

According to one embodiment, a local view frame is generated for each of the lanes 511-513 to represent the driving environment of the lane. Thus, each lane is associated with a sequence of local view frames generated in a series of driving cycles. Once the local view frame is generated, the local view frame is passed to the next processing stage, such as the planning and control stage. The planning and control phases will operate in the same manner whether the positioning is performed in global/absolute mode or local/relative mode.

FIG. 6 is a process flow diagram illustrating stages in a process for operating an autonomous vehicle, according to one embodiment. Referring to fig. 6, process flow 600 includes, among other things, a perception stage 601, a relative coordinate processing stage 602, a prediction stage 603, a planning stage 604, a trajectory generation stage 605, and a control stage 606. The perception stage 601 is primarily performed by the perception module 302 to perceive the driving environment around the ADV, including obtaining images acquired by various sensors (e.g., LID AR, RADAR, camera). The relative coordinate processing stage 602 is primarily performed by the relative coordinate system 308, and the relative coordinate system 308 may be awakened in response to certain circumstances, such as driving on a highway.

As described above, the relative coordinate processing stage 602 determines obstacle information and lane configuration information based on the perception information provided by the perception stage 601, and generates a local view frame based on the obstacle information and the lane configuration information. The local view frame is similar to the positioning data generated by the positioning module 301, but is located at a relative or local coordinate reference point and does not necessarily use map and route information. The prediction phase 603 and planning phase 604 are then similar to the general prediction and planning processes described above with respect to fig. 3A and 3B, but also in relative coordinates. The goal of the planning is to plan the trajectory 605 based on the local view frames so that the ADV can travel along the lane in which the ADV is currently traveling.

The planning phase 604 includes a path planning and optimization phase 604A and a speed planning and optimization phase 604B. In the path planning and optimization stage 604A, a distance traveled-lateral offset (SL) map, such as the SL map 700 shown in fig. 7, is generated based at least in part on the local view frame. Referring now to fig. 7, a SL map 700 acquires obstacles in two dimensions relative to the current position of the ADV. The S dimension refers to the longitudinal distance from the current position of the ADV along the reference line of the lane. The L dimension refers to the lateral distance between the obstacle and the reference line at a particular longitudinal position with respect to the current position of the ADV. It is generally assumed that the ADV is attempting to travel along a reference line and that a positive L value for an obstacle indicates that the obstacle is located to the left of the reference line or ADV. A negative L value for an obstacle indicates that the obstacle is located to the right of the reference line or ADV. In this example, the obstacle 701 is located to the left of the reference line or ADV, while the obstacle 702 is located to the right of the reference line or ADV. SL graph or curve 710 represents the path that the ADV will follow after path planning, in this example, to navigate between obstacles 701 and 702 to avoid a collision.

In the speed planning and optimization stage 704B, a distance-to-travel-time (ST) map, such as the ST map 800 shown in fig. 8, is generated based at least in part on the local view frames. Referring now to fig. 8, an ST graph 800 acquires paths of ADVs in consideration of distances from a current location at different points in time. The S dimension refers to the distance from the current position of the ADV and the T dimension refers to the time. Thus, as shown in fig. 8, curve 810 indicates the location at different points in time in terms of distance from the current location of the ADV. The ST diagram 800 also indicates the relative positions of the obstacles, in this example, the obstacles 801 to 802, at different points in time, with respect to the current position of the ADV. Thus, based on curve 810, the ADV is programmed to track or give way to obstacle 801, but to exceed or cross obstacle 802. Thus, the SL map 700 and ST map 800 may be generated based on local view frames without having to use map data and other common positioning procedures.

FIG. 9 is a flow chart illustrating a process for operating an autonomous vehicle without using map data and positioning, according to one embodiment. Process 900 may be performed by processing logic that may comprise software, hardware, or a combination thereof. For example, process 900 may be performed by system 300 of fig. 3A. Referring to fig. 9, in operation 901, processing logic determines a driving environment around an ADV that is driving in a lane, including sensing at least one obstacle, based on sensor data provided by various sensors mounted on the ADV. In operation 902, the processing logic determines a lane configuration of the lane relative to a current position of the ADV based on the perception data of the driving environment without using map data of a map associated with the lane. In operation 903, the processing logic determines obstacle information of the obstacle relative to the ADV, including a relative position of the obstacle relative to a current position of the ADV. In operation 904, a local view frame is generated based on the lane configuration and the obstacle information of the obstacle. The local view frame includes information describing lane configuration and obstacles from the ADV's perspective without map information. In operation 905, the processing logic generates a trajectory that controls the ADV to travel along the lane in a next driving cycle based on the local view frame.

It should be noted that some or all of the components as shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components may be implemented as software installed and stored in a persistent storage device, which may be loaded into and executed by a processor (not shown) to perform the processes or operations described throughout this application. Alternatively, such components may be implemented as executable code programmed or embedded into dedicated hardware, such as an integrated circuit (e.g., an application specific integrated circuit or ASIC), a Digital Signal Processor (DSP) or Field Programmable Gate Array (FPGA), which is accessible via a respective driver and/or operating system from an application. Further, such components may be implemented as specific hardware logic within a processor or processor core as part of an instruction set accessible by software components through one or more specific instructions.

FIG. 10 is a block diagram illustrating an example of a data processing system that may be used with one embodiment of the present disclosure. For example, system 1500 may represent any of the data processing systems described above that perform any of the processes or methods described above, such as, for example, any of sensing and planning systems 110 or servers 103-104 of fig. 1. System 1500 may include many different components. These components may be implemented as Integrated Circuits (ICs), portions of integrated circuits, discrete electronic devices or other modules adapted for a circuit board, such as a motherboard or add-in card of a computer system, or as components otherwise incorporated within a chassis of a computer system.

It should also be noted that system 1500 is intended to illustrate a high-level view of many components of a computer system. However, it is to be understood that some embodiments may have additional components and, further, other embodiments may have different arrangements of the components shown. System 1500 may represent a desktop computer, a laptop computer, a tablet computer, a server, a mobile phone, a media player, a Personal Digital Assistant (PDA), a smart watch, a personal communicator, a gaming device, a network router or hub, a wireless Access Point (AP) or repeater, a set-top box, or a combination thereof. Further, while only a single machine or system is illustrated, the term "machine" or "system" shall also be taken to include any collection of machines or systems that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

In one embodiment, the system 1500 includes a processor 1501, memory 1503, and devices 1505-1508 connected by a bus or interconnect 1510. Processor 1501 may represent a single processor or multiple processors including a single processor core or multiple processor cores. Processor 1501 may represent one or more general-purpose processors, such as a microprocessor, Central Processing Unit (CPU), or the like. More specifically, processor 1501 may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, or a processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 1501 may also be one or more special-purpose processors, such as an Application Specific Integrated Circuit (ASIC), a cellular or baseband processor, a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a network processor, a graphics processor, a communications processor, a cryptographic processor, a coprocessor, an embedded processor, or any other type of logic capable of processing instructions.

Processor 1501 (which may be a low-power multi-core processor socket such as an ultra-low voltage processor) may serve as a main processing unit and central hub for communicating with the various components of the system. Such a processor may be implemented as a system on a chip (SoC). Processor 1501 is configured to execute instructions for performing the operations and steps discussed herein. The system 1500 may also include a graphics interface to communicate with an optional graphics subsystem 1504, which may include a display controller, a graphics processor, and/or a display device.

Processor 1501 may be in communication with memory 1503, which in one embodiment may be implemented via multiple memory devices to provide a given amount of system storage. The memory 1503 may include one or more volatile storage (or memory) devices such as Random Access Memory (RAM), dynamic RAM (dram), synchronous dram (sdram), static RAM (sram), or other types of storage devices. A memory 1503To store information including sequences of instructions that are executed by processor 1501, or any other device. For example, executable code and/or data for various operating systems, device drivers, firmware (e.g., an input output basic system or BIOS), and/or applications may be loaded into memory 1503 and executed by processor 1501. The operating system may be any type of operating system, for example, a Robotic Operating System (ROS), fromOf a companyOperating System, Mac from apple IncFromOf a companyLINUX, UNIX, or other real-time or embedded operating systems.

System 1500 may also include IO devices such as devices 1505 through 1508 including network interface device 1505, optional input device 1506, and other optional IO devices 1507. Network interface device 1505 may include a wireless transceiver and/or a Network Interface Card (NIC). The wireless transceiver may be a WiFi transceiver, an infrared transceiver, a bluetooth transceiver, a WiMax transceiver, a wireless cellular telephone transceiver, a satellite transceiver (e.g., a Global Positioning System (GPS) transceiver), or other Radio Frequency (RF) transceiver, or a combination thereof. The NIC may be an ethernet card.

The input device 1506 may include a mouse, a touch pad, a touch-sensitive screen (which may be integrated with the display device 1504), a pointing device (such as a stylus) and/or a keyboard (e.g., a physical keyboard or a virtual keyboard displayed as part of the touch-sensitive screen). For example, the input device 1506 may include a touch screen controller coupled to a touch screen. Touch screens and touch screen controllers, for example, may detect contact and movement or discontinuities thereof using any of a variety of touch sensitive technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen.

IO device 1507 may include an audio device. The audio device may include a speaker and/or microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and/or telephony functions. Other IO devices 1507 may also include Universal Serial Bus (USB) ports, parallel ports, serial ports, printers, network interfaces, bus bridges (e.g., PCI-PCI bridges), sensors (e.g., such as accelerometer motion sensors, gyroscopes, magnetometers, light sensors, compasses, proximity sensors, etc.), or combinations thereof. The device 1507 may also include an imaging processing subsystem (e.g., a camera) that may include an optical sensor, such as a Charge Coupled Device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) optical sensor, for facilitating camera functions, such as recording photographs and video clips. Certain sensors can be coupled to interconnect 1510 via a sensor hub (not shown), while other devices, such as a keyboard or thermal sensors, can be controlled by an embedded controller (not shown) depending on the particular configuration or design of system 1500.

To provide persistent storage for information such as data, applications, one or more operating systems, etc., a mass storage device (not shown) may also be coupled to processor 1501. In various embodiments, such mass storage devices may be implemented via Solid State Devices (SSDs) in order to achieve thinner and lighter system designs and improve system responsiveness. However, in other embodiments, the mass storage device may be implemented primarily using a Hard Disk Drive (HDD), with a smaller amount of the SSD storage device acting as an SSD cache to enable non-volatile storage of context state and other such information during a power down event, enabling fast power up upon a system activity restart. Additionally, a flash device may be coupled to processor 1501, for example, via a Serial Peripheral Interface (SPI). Such flash memory devices may provide non-volatile storage of system software, including the BIOS and other firmware of the system.

Storage 1508 may include a computer-accessible storage medium 1509 (also referred to as a machine-readable storage medium or a computer-readable medium) on which is stored one or more sets of instructions or software (e.g., modules, units, and/or logic 1528) embodying any one or more of the methodologies or functions described herein. The processing module/unit/logic 1528 may represent any of the components described above, such as the planning module 305, the control module 306, and/or the relative coordinate system 308. Processing module/unit/logic 1528 may also reside, completely or at least partially, within memory 1503 and/or within processor 1501 during execution thereof by data processing system 1500, memory 1503 and processor 1501, data processing system 1500, memory 1503 and processor 1501 also constituting machine-accessible storage media. Processing module/unit/logic 1528 may also transmit or receive over a network via network interface device 1505.

The computer-readable storage medium 1509 may also be used to permanently store some of the software functions described above. While the computer-readable storage medium 1509 is shown in an exemplary embodiment to be a single medium, the term "computer-readable storage medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term "computer-readable storage medium" shall also be taken to include any medium that is capable of storing or encoding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term "computer-readable storage medium" shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, or any other non-transitory machine-readable medium.

The processing module/unit/logic 1528, components, and other features described herein may be implemented as discrete hardware components or integrated within the functionality of hardware components, such as ASICS, FPGAs, DSPs, or similar devices. Further, the processing module/unit/logic 1528 may be implemented as firmware or functional circuitry within a hardware device. Further, the processing module/unit/logic 1528 may be implemented in any combination of hardware devices and software components.

It should be noted that while system 1500 is illustrated with various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to embodiments of the present disclosure. It will also be appreciated that network computers, hand-held computers, mobile telephones, servers, and/or other data processing systems which have fewer components or perhaps more components may also be used with embodiments of the present disclosure.

Some portions of the foregoing detailed description have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the appended claims, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Embodiments of the present disclosure also relate to apparatuses for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods depicted in the foregoing figures may be performed by processing logic that comprises hardware (e.g., circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations may be performed in a different order. Further, some operations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the disclosure as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.