# Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2306.06987 · HTML: https://mlsanity.com/paper/2306.06987/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 2 of 2)

- [AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving](https://mlsanity.com/paper/2107.06627/) (2021)
- [Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving](https://mlsanity.com/paper/2108.04629/) (2021)

## Built on by (most cited first, 1 of 1)

- [Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification](https://mlsanity.com/paper/2409.15846/) (2024)

## Abstract (arXiv)

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.