Decision-Making Method of Autonomous Vehicles in Urban Environments Considering Traffic Laws

Lu, Xinghao; Hai-yan, Zhao; Gao, Bingzhao; Chen, Weixuan; Chen, Hong

doi:10.1109/tits.2022.3183229

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…These predominantly encompass discrete input space planning based on collision detection [14], finite state machine [15], constraint optimization [16], and reachable sets [17]. Lu et al [18] utilized static traffic laws as the foundation for generating a set of candidate behaviors. It comprehensively considers factors such as efficiency and safety, allowing vehicles to determine the optimal decision while complying with traffic laws and having high consistency with manual decision results.…”

Section: Related Workmentioning

confidence: 99%

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Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

Mu,

Yu,

et al. 2024

Applied Sciences

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In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.

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Section: Related Workmentioning

confidence: 99%

“…TTC std is the standard collision time. The TTC std values are defined as 2.5 s, 3 s, and 3.5 s when the relative speed falls below 10 m/s, between 10-15 m/s, and exceeds 15 m/s, respectively [18].…”

Section: Collision Riskmentioning

confidence: 99%