阅读说明：本技术 道路场景划分方法、装置、电子设备及存储介质 (Road scene dividing method and device, electronic equipment and storage medium ) 是由 王志军 孙立光 于 2019-02-12 设计创作，主要内容包括：本发明涉及一种道路场景划分方法及装置、电子设备及存储介质,属于信息处理技术领域。所述道路场景划分方法包括：获取多个局部道路的道路属性信息；确定多个主题,并依据各所述局部道路的道路属性信息计算各所述局部道路在每一主题下的概率分布数据；以各所述局部道路的所述概率分布数据作为特征向量,对各所述局部道路进行聚类；将位于同一聚类簇的所述局部道路,划分至同一道路场景。本发明可以以更加合适的粒度实现道路场景划分。(The invention relates to a road scene dividing method and device, electronic equipment and a storage medium, and belongs to the technical field of information processing. The road scene division method comprises the following steps: acquiring road attribute information of a plurality of local roads; determining a plurality of themes, and calculating probability distribution data of each local road under each theme according to the road attribute information of each local road; clustering each local road by taking the probability distribution data of each local road as a feature vector; and dividing the local roads in the same cluster into the same road scene. The invention can realize road scene division with more proper granularity.)

1. A road scene division method is characterized by comprising the following steps:

acquiring road attribute information of a plurality of local roads;

determining a plurality of themes, and calculating probability distribution data of each local road under each theme according to the road attribute information of each local road;

clustering each local road by taking the probability distribution data of each local road as a feature vector;

and dividing the local roads in the same cluster into the same road scene.

2. The road scene division method according to claim 1, further comprising:

acquiring road attribute information of a target local road;

calculating probability distribution data of the target local road under each theme according to the road attribute information of the target local road;

determining a target cluster to which the target local road belongs by taking the probability distribution data of the target local road as a feature vector;

and taking the road scene corresponding to the target cluster as the road scene of the target local road.

3. The road scene division method according to claim 2, wherein determining the target cluster to which the target local road belongs comprises:

acquiring a clustering center of each clustering cluster;

calculating the distance value between the feature vector of the target local road and each cluster center;

and taking the cluster where the cluster center corresponding to the minimum distance value is located as the target cluster.

4. The road scene division method according to claim 2, further comprising:

and after the target local road is divided into the target cluster, recalculating the cluster center of the target cluster.

5. The road scene division method according to claim 1, wherein the road attribute information of the local road includes one or more of:

the road network basic attribute information of the local road, the topological attribute information of the local road and the excavation attribute information of the local road.

6. The road scene division method according to any one of claims 1 to 5, wherein obtaining probability distribution data of each of the local roads with respect to each topic comprises:

forming an attribute information set by using all road attribute information of all local roads, and randomly distributing an initial theme for each attribute information in the set;

executing the following cyclic process until the theme distribution of each local road and the attribute information distribution under each theme are converged: counting attribute information distribution under each theme and theme distribution of each local road; taking attribute information of an unrefreshed subject in the set as current attribute information; updating the theme for the current attribute information according to the theme distribution of all attribute information except the current attribute information in the set;

and calculating probability distribution data of each local road related to each topic according to the topic distribution of each local road when the cyclic process is executed.

7. The road scene division method according to claim 6, wherein updating a theme for the current attribute information includes:

calculating the transition probability of the current attribute information to each topic according to the topic distribution of all attribute information except the current attribute information in the set;

and sampling a new theme for the current attribute information again according to the transition probability.

8. The road scene division method of claim 1, wherein clustering each of the local roads comprises:

selecting a preset number of local roads as initial clustering centers; and

executing the following loop process until the clustering termination condition is met:

selecting an unclustered local road as a current local road;

calculating the distance between the current local road and each current clustering center according to the feature vector; and

and distributing the current local road to the nearest clustering center, and recalculating the clustering center after distribution.

9. The road scene division method according to any one of claims 1 to 5 or 7 to 8, further comprising:

respectively determining different road condition calculation strategies for each divided road scene;

and verifying the division result of the road scene according to the calculation result of each road condition calculation strategy.

10. A road scene division apparatus, comprising:

the first information acquisition module is used for acquiring road attribute information of a plurality of local roads;

the first theme classification module is used for determining a plurality of themes and calculating probability distribution data of each local road under each theme according to the road attribute information of each local road;

the local road clustering module is used for clustering each local road by taking the probability distribution data of each local road as a feature vector;

and the first scene division module is used for dividing the local roads in the same cluster into the same road scene.

11. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the method of any of claims 1-9 via execution of the executable instructions.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1-9.