[论文解读] Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving

Daofei Li, Ao Liu+2 more|arXiv (Cornell University)|Nov 4, 2021

Autonomous Vehicle Technology and Safety被引用 4

一句话总结

本文提出了一种基于博弈论的自动驾驶车辆（AV）决策算法，使AV在无信号灯交叉路口安全高效地通行，同时在与人类驾驶卡车的交互中尊重社会规范。通过建模卡车司机的有限视觉范围，并将社会契合度与互惠利他主义纳入收益设计，该算法在24名受试者参与的207起交互案例的人在回路实验中，提升了安全性、效率以及人类驾驶员的预期满足度。

ABSTRACT

Safe and smooth interacting with other vehicles is one of the ultimate goals of driving automation. However, recent reports of demonstrative deployments of automated vehicles (AVs) indicate that AVs are still difficult to meet the expectation of other interacting drivers, which leads to several AV accidents involving human-driven vehicles (HVs). This is most likely due to the lack of understanding about the dynamic interaction process, especially about the human drivers. By investigating the causes of 4,300 video clips of traffic accidents, we find that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents, especially in those involving trucks. A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection. Starting from a probabilistic model for the visual field characteristics of truck drivers, social fitness and reciprocal altruism in the decision are incorporated in the game payoff design. Human-in-the-loop experiments are carried out, in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms. Totally 207 cases of intersection interactions are obtained and analyzed, which shows that the proposed decision-making algorithm can not only improve both safety and time efficiency, but also make AV decisions more in line with the expectation of interacting human drivers. These findings can help inform the design of automated driving decision algorithms, to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.

研究动机与目标

• 解决AV在无信号灯交叉路口交互中未能满足人类驾驶员预期的挑战。
• 探究卡车司机有限视觉范围如何导致车辆间事故，尤其是涉及AV的事故。
• 设计一种决策算法，以增强AV与人类车辆（HV）交互中的安全性、效率与社会兼容性。
• 评估将社会契合度与互惠利他主义纳入算法是否能改善人类驾驶员对AV行为的感知。
• 通过真实驾驶员与AV交互的人在回路实验，提供实证验证。

提出的方法

• 开发了一种概率模型，以表征卡车司机在交叉路口交互过程中动态视觉范围的限制。
• 设计了一种博弈论框架，AV决策通过整合社会契合度与互惠利他主义的收益函数，实现社会兼容性。
• 该算法利用对人类驾驶卡车轨迹的实时感知与预测，指导AV的战略决策。
• 收益设计结合了安全性（碰撞规避）与社会因素（让行行为、交替通行预期），以引导AV行为。
• 通过驾驶模拟器进行的人在回路实验，模拟了207起无信号灯交叉路口的交互。
• 对比了三种算法——所提算法、基线算法与保守算法，评估其在安全性、效率与社会兼容性指标上的表现。

实验结果

研究问题

• RQ1 卡车司机的有限视觉范围在无信号灯交叉路口交互中如何导致事故？
• RQ2 在人在回路设置中，将社会契合度与互惠利他主义纳入在多大程度上改善了AV的决策？
• RQ3 基于博弈论的AV决策算法能否在提升安全性和效率的同时，与人类驾驶员的预期保持一致？
• RQ4 人类驾驶员如何感知并回应体现社会兼容性的AV行为，与纯粹基于安全或效率驱动的策略相比？
• RQ5 在复杂交叉路口场景中，视觉范围建模对AV决策性能的相对影响是什么？

主要发现

• 所提算法显著提升了安全性，降低了无信号灯交叉路口交互中的碰撞风险。
• 与基线和保守算法相比，时间效率得到提升，决策更迅速且更具可预测性。
• 人类驾驶员报告称，当引入社会兼容性时，对AV行为的接受度更高，且感知更自然。
• 该算法在与人类预期对齐方面优于其他算法，尤其在交替通行与让行行为方面表现更佳。
• 视觉范围建模的整合提升了AV对人类卡车司机行为预测的现实感与准确性。
• 人在回路实验证实，社会兼容性是AV与HV交互成功的关键因素。