Autonomous driving paper index
Evolutionary game analysis of liability boundaries in L3 autonomous driving: an electrical engineering perspective
One-line summary
This paper proposes a Human-Machine Cyber-Physical System (HM-CPS) framework to investigate the evolutionary strategic choices between human drivers and autonomous driving algorithms using an evolutionary game model.
Engineering notes
Key topics: autonomous driving, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract The commercialization of Level 3 (L3) conditionally automated vehicles introduces critical challenges in human-machine shared control. This paper proposes a Human-Machine Cyber-Physical System (HM-CPS) framework to investigate the evolutionary strategic choices between human drivers and autonomous driving algorithms using an evolutionary game model. We systematically analyze interactive behaviors under the influence of psychological risk compensation effects, legal liability allocation, and algorithmic computational costs. Theoretical analysis and numerical simulations reveal that extreme legal liability boundaries trigger “free-riding” behaviors, driving the system toward suboptimal equilibrium states where one party becomes overly reliant on the other. Furthermore, we demonstrate that high computational burdens force autonomous systems into aggressive states, compromising overall safety margins. These findings provide data-driven, quantitative guidelines for policymakers and automotive engineers to optimally calibrate liability frameworks and allocate onboard computing resources, ultimately fostering a safe and trustworthy human-machine co-driving ecosystem.
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