Abstract: A method of forecasting risk-biased trajectories of agents surrounding an ego vehicle is described. The method includes sampling a risk-neutral latent space generated by a trained encoder of a generative network based on past surrounding agent trajectories. The method also includes predicting, based on the sampling of the risk-neutral latent space, risk-neutral future surrounding agent trajectories using a trained decoder of the generative network. The method further includes sampling a risk-biased latent space distribution generated by a trained, risk-aware encoder of the generative network based on past trajectories of the ego vehicle and a risk-sensitivity. The method also includes predicting, based on the sampling of the risk-biased latent space distribution, risk-biased future surrounding agent trajectories using the trained decoder of the generative network.

Abstract: A method of generating an output trajectory of an ego vehicle includes recording trajectory data of the ego vehicle and pedestrian agents from a scene of a training environment of the ego vehicle. The method includes identifying at least one pedestrian agent from the pedestrian agents within the scene of the training environment of the ego vehicle causing a prediction-discrepancy by the ego vehicle greater than the pedestrian agents within the scene. The method includes updating parameters of a motion prediction model of the ego vehicle based on a magnitude of the prediction-discrepancy caused by the at least one pedestrian agent on the ego vehicle to form a trained, control-aware prediction objective model. The method includes selecting a vehicle control action of the ego vehicle in response to a predicted motion from the trained, control-aware prediction objective model regarding detected pedestrian agents within a traffic environment of the ego vehicle.