Abstract: Described herein are systems and methods for determining a trajectory for a vehicle. In one example, a system includes a processor and a memory in communication with the processor having a planning module. The planning module includes instructions that, when executed by the processor, cause the processor to determine, using a unified neural network based on input information, ego vehicle future trajectories of the ego vehicle and agent future trajectories of one or more agents, select one of the ego vehicle future trajectories as a selected trajectory based on the ego vehicle future trajectories and agent future trajectories using a cost function, and cause the ego vehicle to execute the selected trajectory.

Abstract: Systems and methods for training a policy are disclosed. In one example, a system includes a processor and a memory with instructions that cause the processor to train the policy using a training data set with training scenes to generate an identification policy and perform a closed-loop simulation on the identification policy to collect closed-loop metrics. Based on the closed-loop metrics, the instructions cause the processor to construct an error set of the training scenes and construct an upsampled training set by upsampling the error set. After that, the policy is trained using the upsampled training set to generate a final policy.