  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-to-vehicle (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.
