Uncertainty-Aware Optimization for Physics-Aware Highway Trajectory Prediction
The framework proposes an uncertainty-aware optimization method for physics-aware highway trajectory prediction. It models both aleatoric (inherent randomness) and epistemic (model knowledge gaps) uncertainties in vehicle motion variables. By propagating these uncertainties through a kinematic rollout, the method generates probabilistic trajectories, allowing for reliable safety-critical predictions.