Adaptive Field Effect Planner for Safe Interactive Autonomous Driving on Curved Roads
Qinghao Li, Zhen Tian, Xiaodan Wang, Jinming Yang, Zhihao Lin
TL;DR
This work tackles safe interactive autonomous driving on curved roads by integrating an adaptive risk-field derived from artificial potential fields, Frenet-frame trajectory planning, and an IPSO-based quintic polynomial trajectory optimizer. The risk field combines lane-keeping attraction, front-vehicle repulsion, and lane-change risk to dynamically adjust lane-change decisions in response to surrounding HDVs, while Frenet coordinates simplify planning on non-straight roads. An IPSO-enhanced quintic polynomial generator selects dynamically feasible, smooth trajectories within input limits, balancing safety and user comfort. Simulations on curvy roads demonstrate effective handling of dynamic obstacles, maintenance of safety margins, and faster convergence than benchmark optimizers, highlighting practical potential for real-time, curved-road autonomous navigation.
Abstract
Autonomous driving has garnered significant attention for its potential to improve safety, traffic efficiency, and user convenience. However, the dynamic and complex nature of interactive driving poses significant challenges, including the need to navigate non-linear road geometries, handle dynamic obstacles, and meet stringent safety and comfort requirements. Traditional approaches, such as artificial potential fields (APF), often fall short in addressing these complexities independently, necessitating the development of integrated and adaptive frameworks. This paper presents a novel approach to autonomous vehicle navigation that integrates artificial potential fields, Frenet coordinates, and improved particle swarm optimization (IPSO). A dynamic risk field, adapted from traditional APF, is proposed to ensure interactive safety by quantifying risks and dynamically adjusting lane-changing intentions based on surrounding vehicle behavior. Frenet coordinates are utilized to simplify trajectory planning on non-straight roads, while an enhanced quintic polynomial trajectory generator ensures smooth and comfortable path transitions. Additionally, an IPSO algorithm optimizes trajectory selection in real time, balancing safety and user comfort within a feasible input range. The proposed framework is validated through extensive simulations and real-world scenarios, demonstrating its ability to navigate complex traffic environments, maintain safety margins, and generate smooth, dynamically feasible trajectories.