Trajectory Planning of Autonomous Vehicles Based on Parameterized Control Optimization in Dynamic on-Road Environments

作     者：Zhu, Sheng Aksun-Guvenc, Bilin 

作者机构：Ohio State Univ Automated Driving Lab Columbus OH 43212 USA Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43212 USA 

出 版 物：《JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS》 (智能和机器人系统杂志)

年 卷 期：2020年第100卷第3-4期

页      面：1055-1067页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Smart Campus organization of the Ohio State University Smart Columbus 

主　　题：Autonomous vehicles Trajectory planning Control parameterization Moving objects 

摘      要：This paper presents a trajectory planning framework to deal with the highly dynamic environments for on-road driving. The trajectory optimization problem with parameterized curvature control was formulated to reach the goal state with the vehicle model and its dynamic constraints considered. This in contrast to existing curve fitting techniques guarantees the dynamic feasibility of the planned trajectory. With generation of multiple trajectory candidates along the Frenet frame, the vehicle is reactive to other road users or obstacles encountered. Additionally, to deal with more complex driving scenarios, its seamless interaction with an upper behavior planning layer was considered by having longitudinal motion planning responsive to the desired goal state. The trajectory evaluation and selection methodologies, along with the low-level tracking control, were also developed under this framework. The potential of the proposed trajectory planning framework was demonstrated under different dynamic driving scenarios such as lane-changing or merging with surrounding vehicles with its computation efficiency proven in real-time simulations.