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How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction? by Shao, Wenbo; Xu, Yanchao; Li, Jun; Lv, Chen; Wang, Weida; Wang, Hong is a scholarly article available to read on EtoBox.

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An accurate trajectory prediction is crucial for safe and efficient autonomous driving in complex traffic environments. In recent years, artificial intelligence has shown strong capabilities in improving prediction accuracy. However, its characteristics of inexplicability and uncertainty make it challenging to determine the traffic environmental effect on prediction explicitly, posing significant challenges to safety-critical decision-making. To address these challenges, this study proposes a trajectory prediction framework with the epistemic uncertainty estimation ability that outputs high uncertainty when confronting unforeseeable or unknown scenarios. The proposed framework is used to analyze the environmental effect on the prediction algorithm performance. In the analysis, the traffic environment is considered in terms of scenario features and shifts, respectively, where features are divided into kinematic features of a target agent, features of its surrounding traffic participants, and other features. In addition, feature correlation and importance analyses are performed to study the above features' influence on the prediction error and epistemic uncertainty. Further, a cross-

Author

Shao, Wenbo; Xu, Yanchao; Li, Jun; Lv, Chen; Wang, Weida; Wang, Hong

Published

2023

Language

EN