How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?

Abstract

Accurate trajectory prediction is essential for safe and efficient autonomous driving in complex traffic environments. While artificial intelligence has shown great promise in improving prediction accuracy, its inherent uncertainty and lack of explainability may lead to unpredictable failures, creating challenges for safety-critical decision-making. This study aims to address these challenges by exploring the impact of traffic environment on prediction algorithms. The study proposes a trajectory prediction framework with epistemic uncertainty estimation ability to output high uncertainty when facing unforeseeable or unknown scenarios. The framework analyzes the environmental effect on the trajectory prediction by considering scenario features and shifts. Features are divided into kinematic features of a target agent, features of surrounding traffic participants, and other scenario features. Feature correlation and importance analyses are performed to study their influence on prediction error and epistemic uncertainty. The impact of unavoidable distributional shifts in the real world on trajectory predictions is investigated using multiple intersection datasets. The results indicate that deep ensemble-based methods have advantages in improving robustness while estimating epistemic uncertainty. Consistent conclusions were obtained from the correlation and importance analyses, indicating that kinematic features of the target agent have relatively strong effects on both prediction error and epistemic uncertainty. Finally, the study analyzes the accuracy deterioration caused by distributional shifts and the potential of the deep ensemble-based method. Through deep ensemble, the errors of the prediction methods based on GRIP++ and Trajectron++ have been improved by 6.4% and 10.8% in the same-dataset test, and 6.3% and 10.8% in the cross-dataset test.