Online legal driving behavior monitoring for self-driving vehicles

Wenhao Yu, Chengxiang Zhao, Hong Wang (), Jiaxin Liu, Xiaohan Ma, Yingkai Yang, Jun Li, Weida Wang, Xiaosong Hu () and Ding Zhao
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Wenhao Yu: Tsinghua University
Chengxiang Zhao: Beijing Institute of Technology
Hong Wang: Tsinghua University
Jiaxin Liu: Tsinghua University
Xiaohan Ma: Beijing Institute of Technology
Yingkai Yang: Tsinghua University
Jun Li: Tsinghua University
Weida Wang: Beijing Institute of Technology
Xiaosong Hu: Chongqing University
Ding Zhao: Carnegie Mellon University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Defined traffic laws must be respected by all vehicles when driving on the road, including self-driving vehicles without human drivers. Nevertheless, the ambiguity of human-oriented traffic laws, particularly compliance thresholds, poses a significant challenge to the implementation of regulations on self-driving vehicles, especially in detecting illegal driving behaviors. To address these challenges, here we present a trigger-based hierarchical online monitor for self-assessment of driving behavior, which aims to improve the rationality and real-time performance of the monitoring results. Furthermore, the general principle to determine the ambiguous compliance threshold based on real driving behaviors is proposed, and the specific outcomes and sensitivity of the compliance threshold selection are analyzed. In this work, the effectiveness and real-time capability of the online monitor were verified using both Chinese human driving behavior datasets and real vehicle field tests, indicating the potential for implementing regulations in self-driving vehicles for online monitoring.

Date: 2024
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DOI: 10.1038/s41467-024-44694-5

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