SYSTEMS AND METHODS FOR DETERMINING DRIVING PATH IN AUTONOMOUS DRIVING

1 . A system for determining a driving path in autonomous driving, comprising:

at least one storage medium including a set of instructions; and

at least one processor in communication with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to cause the system to:

obtain a plurality of candidate driving paths;

obtain one or more coefficients associated with the plurality of candidate driving paths based on a trained coefficient-generating model;

determine a travel cost for each of the plurality of candidate driving paths based on the on one or more coefficients; and

identify a target driving path from the plurality of candidate driving paths based on a plurality of travel costs corresponding to the plurality of candidate driving paths.

2 . The system of claim 1 , wherein to determine the travel cost for each of the plurality of candidate driving paths, the at least one processor is directed to cause the system to:

determine one or more cost parameters; and

determine the travel cost for each of the plurality of candidate driving paths based on the one or more cost parameters and the one or more coefficients.

3 . The system of claim 2 , wherein the one or more cost parameters include at least one of a speed cost parameter, a similarity cost parameter, or a jerk cost parameter.

4 . The system of claim 2 , wherein the one or more cost parameters include a speed cost parameter, a similarity cost parameter, and a jerk cost parameter.

5 . The system of claim 1 , wherein the trained coefficient-generating model is determined with a training process, the training process comprising:

obtaining a plurality of sample driving paths;

determining a plurality of samples based on the plurality of sample driving paths, wherein each of the plurality of samples includes a set of sample driving paths corresponding to a same start location and a same destination;

for each of the plurality of samples, determining a set of sample scores corresponding to the set of sample driving paths; and

determining the trained coefficient-generating model based on the scores of the plurality of samples.

6 . The system of claim 5 , wherein the determining the trained coefficient-generating model based on the plurality of samples includes:

obtaining a preliminary coefficient-generating model including a plurality of preliminary coefficients, wherein each of the plurality of preliminary coefficients corresponds to a sample;

extracting feature information of each of the plurality of samples;

for each of the plurality of samples, determining a set of sample travel costs corresponding to the set of sample driving paths based on a corresponding preliminary coefficient and the feature information;

determining whether a plurality of sets of sample travel costs and a plurality of sets of sample scores corresponding to the plurality of samples satisfy a preset condition; and

designating the preliminary coefficient-generating model as the trained coefficient-generating model in response to the determination that the plurality of sets of sample travel costs and the plurality of sets of sample scores satisfy the preset condition.

7 . The system of claim 6 , wherein the determining the trained coefficient-generating model based on the plurality of samples further includes:

updating the plurality of preliminary coefficients in response to the determination that the plurality of sets of sample travel costs and the plurality of sets of sample scores do not satisfy the preset condition, and

repeating the step of determining whether a plurality of sets of sample travel costs and a plurality of sets of sample scores corresponding to the plurality of samples satisfy the preset condition.

8 . The system of claim 6 , wherein the feature information of each of the plurality of samples includes velocity information of each of the set of sample driving paths and obstacle information associated with each of the set of sample driving paths.

9 . The system of claim 1 , wherein to identify the target driving path from the plurality of candidate driving paths based on the plurality of travel costs corresponding to the plurality of candidate driving paths, at least one processor is directed to cause the system to:

identify a smallest travel cost from the plurality of travel costs; and

identify a candidate driving path corresponding to the smallest travel cost as the target driving path.

10 . The system of claim 1 , wherein the at least one processor is directed to cause the system further to:

transmit the target driving path to one or more control elements of a vehicle, directing the vehicle to follow the target driving path.

11 . A method implemented on a computing device having at least one processor, at least one storage medium, and a communication platform connected to a network, the method comprising:

obtaining a plurality of candidate driving paths;

obtaining one or more coefficients associated with the plurality of candidate driving paths based on a trained coefficient-generating model;

determining a travel cost for each of the plurality of candidate driving paths based on the on one or more coefficients; and

identifying a target driving path from the plurality of candidate driving paths based on a plurality of travel costs corresponding to the plurality of candidate driving paths.

12 . The method of claim 11 , wherein the determining the travel cost for each of the plurality of candidate driving paths includes:

determining one or more cost parameters; and

determining the travel cost for each of the plurality of candidate driving paths based on the one or more cost parameters and the one or more coefficients.

13 . The method of claim 12 , wherein the one or more cost parameters include at least one of a speed cost parameter, a similarity cost parameter, or a jerk cost parameter.

14 . The method of claim 12 , wherein the one or more cost parameters include a speed cost parameter, a similarity cost parameter, and a jerk cost parameter.

15 . The method of claim 11 , wherein the trained coefficient-generating model is determined with a training process, the training process comprising:

obtaining a plurality of sample driving paths;

determining a plurality of samples based on the plurality of sample driving paths, wherein each of the plurality of samples includes a set of sample driving paths corresponding to a same start location and a same destination;

for each of the plurality of samples, determining a set of sample scores corresponding to the set of sample driving paths; and

determining the trained coefficient-generating model based on the scores of the plurality of samples.

16 . The method of claim 15 , wherein the determining the trained coefficient-generating model based on the plurality of samples includes:

obtaining a preliminary coefficient-generating model including a plurality of preliminary coefficients, wherein each of the plurality of preliminary coefficients corresponds to a sample;

extracting feature information of each of the plurality of samples;

for each of the plurality of samples, determining a set of sample travel costs corresponding to the set of sample driving paths based on a corresponding preliminary coefficient and the feature information;

determining whether a plurality of sets of sample travel costs and a plurality of sets of sample scores corresponding to the plurality of samples satisfy a preset condition; and

designating the preliminary coefficient-generating model as the trained coefficient-generating model in response to the determination that the plurality of sets of sample travel costs and the plurality of sets of sample scores satisfy the preset condition.

17 . The method of claim 16 , wherein the determining the trained coefficient-generating model based on the plurality of samples further includes:

updating the plurality of preliminary coefficients in response to the determination that the plurality of sets of sample travel costs and the plurality of sets of sample scores do not satisfy the preset condition, and

repeating the step of determining whether a plurality of sets of sample travel costs and a plurality of sets of sample scores corresponding to the plurality of samples satisfy the preset condition.

18 . The method of claim 16 , wherein the feature information of each of the plurality of samples includes velocity information of each of the set of sample driving paths and obstacle information associated with each of the set of sample driving paths.

19 . The method of claim 11 , wherein the identifying the target driving path from the plurality of candidate driving paths based on the plurality of travel costs corresponding to the plurality of candidate driving paths includes:

identifying a smallest travel cost from the plurality of travel costs; and

identifying a candidate driving path corresponding to the smallest travel cost as the target driving path.

20 . (canceled)

21 . A vehicle configured for autonomous driving, comprising:

a detecting component, a planning component, and a control component, wherein the planning component is configured to:

obtain a plurality of candidate driving paths;

obtain one or more coefficients associated with the plurality of candidate driving paths based on a trained coefficient-generating model;

determine a travel cost for each of the plurality of candidate driving paths based on the on one or more coefficients; and

identify a target driving path from the plurality of candidate driving paths based on a plurality of travel costs corresponding to the plurality of candidate driving paths.