Driving comfort significantly affects travel experiences. However, the most prevailing automated vehicle (AV) control strategies mainly focus on the safety and efficiency concerns, neglecting the comfort perspective. Moreover, pavement conditions, such as roughness and distress, cannot be directly detected by standard AV onboard sensors, although they are essential factors impacting driving comfort. This study aimed to present a vehicle-to-infrastructure system to adjust AV trajectories for alleviating the negative impacts of bumpy roads on comfort. The precollected lane-based roadway roughness information was used to optimally adjust the longitudinal and lateral AV trajectories. The method was validated by an eight-degree-of-freedom full-car simulation model. Specifically, the relationships among comfort, jerk, speed, and pavement parameters were revealed and applied to the adjustment process. The results showed that the trajectory adjustment approach improved the overall comfort level by 30 % in the numerical case. Furthermore, a framework was designed to facilitate data exchanges between vehicles and infrastructures in empirical scenarios. The proposed method had the potential to assist AVs in better-perceived driving quality.

驾驶舒适度显着影响出行体验。然而，最流行的自动驾驶汽车 (AV) 控制策略主要关注安全和效率问题，而忽略了舒适性。此外，标准的 AV 车载传感器无法直接检测路面状况，例如粗糙度和破损，尽管它们是影响驾驶舒适性的重要因素。本研究旨在提出一种车辆到基础设施系统来调整自动驾驶轨迹，以减轻颠簸道路对舒适性的负面影响。预先收集的基于车道的道路粗糙度信息用于优化调整纵向和横向 AV 轨迹。该方法得到了八自由度整车仿真模型的验证。具体来说，舒适度、挺度、速度之间的关系，路面参数被揭示并应用于调整过程。结果表明，在数值案例中，轨迹调整方法使整体舒适度提高了 30%。此外，还设计了一个框架，以促进经验情景中车辆和基础设施之间的数据交换。所提出的方法有可能帮助自动驾驶汽车获得更好的驾驶质量。