Recent transportation research suggests that autonomous vehicles (AVs) have the potential to improve traffic flow efficiency as they are able to maintain smaller car-following distances. Nevertheless, being a unique class of ground robots, AVs are susceptible to robotic errors, particularly in their perception module, leading to uncertainties in their movements and an increased risk of collisions. Consequently, conservative operational strategies, such as larger headway and slower speeds, are implemented to prioritize safety over traffic capacity in real-world operations. To reconcile the inconsistency, this paper proposes an analytical model framework that delineates the endogenous reciprocity between traffic safety and efficiency that arises from robotic uncertainty in AVs. Car-following scenarios are extensively examined, with uncertain headway as the key parameter for bridging the single-lane capacity and the collision probability. A Markov chain is then introduced to describe the dynamics of the lane capacity, and the resulting expected collision-inclusive capacity is adopted as the ultimate performance measure for fully autonomous traffic. With the help of this analytical model, it is possible to support the settings of critical parameters in AV operations and incorporate optimization techniques to assist traffic management strategies for autonomous traffic.

翻译：近期交通研究表明，自动驾驶车辆（AVs）凭借其维持更小跟车距离的能力，有潜力提升交通流效率。然而，作为一类特殊的地面机器人，AVs易受机器人误差影响，特别是在其感知模块中，导致其运动存在不确定性并增加碰撞风险。因此，在实际运营中，为确保安全优先于通行能力，往往采用保守运行策略（如更大车头时距与更低车速）。为调和这一矛盾，本文提出一个分析框架模型，系统刻画由AVs机器人不确定性引发的交通安全与效率之间的内在互惠关系。通过深入分析跟车场景，将不确定车头时距作为连接单车道通行能力与碰撞概率的关键参数；进而引入马尔可夫链描述车道通行能力动态演化过程，并将所得期望碰撞包含通行能力作为完全自主交通的终极性能指标。借助该分析模型，可支持AVs关键参数配置设定，并融合优化技术助力自主交通管理策略的制定。