Physics-aware system predicts vehicle paths with uncertainty estimates
Uncertainty-Aware Optimization for Physics-Aware Highway Trajectory Prediction
Machine Learning
Summary
Accurately predicting where cars will go next is very important for self-driving cars to be safe. Most existing methods only guess one exact future path, but this team figured out how to also measure how uncertain those guesses are, by looking closely at the car’s motion and how it moves. They improved a system called X-TRACK so it tells both where a car might go and how sure it is about that prediction. This helps make better, more reliable driving decisions because the car knows when it’s less certain about its predictions.
What this means in practice
- •For autonomous vehicle engineers: Improve self-driving software by integrating physics-aware uncertainty estimates for safer and more reliable path planning.
- •For robotic navigation developers: Enhance mobile robot motion forecasting with calibrated uncertainty that considers robot dynamics for better operational reliability.
Authors
Aanchal Rajesh Chugh, Sebastian Dorn
Abstract
Accurate trajectory forecasting and well-defined predictive uncertainty are crucial for reliable, safety-critical applications such as autonomous driving. Most trajectory prediction approaches provide point estimates only, while uncertainty-aware approaches typically quantify uncertainty only in the trajectory space. In physics-aware approaches, uncertainty in the predicted motion variables should be explicitly modeled and propagated through the vehicle dynamics. Otherwise, the resulting trajectory-space uncertainty may not fully reflect the variability introduced by the underlying motion prediction. Therefore, in this work, uncertainty-aware extensions of X-TRACK (X-TRACK-DE and X-TRACK-MCD), a physics-aware trajectory prediction framework, are proposed. The proposed framework predicts future vehicle motion variables and models both aleatoric and epistemic uncertainties by propagating motion space uncertainty to trajectory space. Additionally, conformal prediction is applied to the trajectory space predictive covariance to construct uncertainty regions targeting a desired marginal coverage level. Evaluation on the highD dataset shows that X-TRACK-DE improves trajectory prediction accuracy over the deterministic baseline, while both uncertainty-aware variants provide predictive uncertainty that can be conformally calibrated to the desired marginal coverage level.