Robust Vehicle Trajectory Prediction via Counterfactual Intervention for Autonomous Driving

The trajectory prediction module of autonomous driving navigation systems is required to correctly forecast the future trajectories of other agents and make correct driving decisions to guarantee the safety of self-driving vehicles. However, the trajectory prediction module is susceptible to adversarial attacks, which can lead to wrong driving decisions. Here, we propose RVTP, a robust prediction method against adversarial attacks for trajectory prediction, which is grounded in causal inference. RVTP employs counterfactual intervention to remove the influence of adversarial perturbations on observed history trajectories. Firstly, we present four evaluation metrics to measure directed attacks from four directions under adversarial attacks. Secondly, we establish the causal graph in attack scenarios and study the causal relationships of various elements under attack. Thirdly, we implement the counterfactual intervention based on the causal graph to calculate the causal effect for mitigating the influence of adversarial attacks. Compared with other trajectory prediction methods under attack, extensive experiments demonstrate that RVTP achieves enhanced performance under attacks at the cost of a minimal performance decrease in no attack scenario.