| 제목 |
COMFORT-BASED TRAJECTORY AND VELOCITY PLANNING FOR AUTOMATED VEHICLES CONSIDERING ROAD CONDITIONS |
| 분야 |
ADAS, AI, Autonomous Vehicles |
| 언어 |
English |
| 저자 |
Shengyu Yan(Chang’an University), Chenglong Liu(Tongji University), Jing Cao(Tongji University) |
| Key Words |
Automated vehicles, Comfort improvement, Full-car simulation, Trajectory control, V2I communication |
| 초록 |
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. |
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