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A novel potential field-based model curve fitting method (PF-MCF) is presented in this paper to handle emergency collision avoidance in waypoint tracking (following waypoints from a leading vehicle). It is reported that the PF has high performance on real-time obstacle avoidance in robotic path planning. However, some inherent limitations exist in the PF, such as no passage between closely spaced obstacles and oscillations in the presence of obstacles, which is rarely discussed in the research field of PF-based autonomous vehicles (AVs), especially in waypoint tracking. To solve these problems, we propose the PF-MCF method to transform the clothoid curve into a quadratic programming form and solve the optimization under reasonable constraints to consider the different waypoints from the PF and the leading vehicle. The proposed PF-MCF method is validated via MATLAB/Simulink and CarSim to avoid multiple obstacle vehicles in waypoint tracking compared with the two latest methods (PF-based model predictive controller and PF-based curve fitting method). The simulation results confirm the efficiency of finding a passage in closely spaced obstacle vehicles and eliminating oscillations (under 0.5 [deg] in tire steering angle) in the presence of obstacles compared to other latest methods.

Existing potential functions (PFs) utilized in autonomous vehicles mainly focus on solving the path-planning problems in some conventional driving scenarios; thus, their performance may not be satisfactory in the context of emergency obstacle avoidance. Therefore, we propose a novel model predictive path-planning controller (MPPC) combined with PFs to handle complex traffic scenarios (e.g., emergency avoidance when a sudden accident occurs). Specifically, to enhance the safety of the PFs, we developed an MPPC to handle an emergency case with a sigmoid-based safe passage embedded in the MPC constraints (SPMPC) with a specific triggering analysis algorithm on monitoring traffic emergencies. The presented PF-SPMPC algorithm was compiled in a comparative simulation study using MATLAB/Simulink and CarSim. The algorithm outperformed the latest PF-MPC approach to eliminate the severe tire oscillations and guarantee autonomous driving safety when handling the traffic emergency avoidance scenario.

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-tovehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.