# Developing Path Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging

arXiv: https://arxiv.org/abs/2409.05289 · HTML: https://mlsanity.com/paper/2409.05289/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning](https://mlsanity.com/paper/2107.08325/) (2021)
- [MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts](https://mlsanity.com/paper/2303.00638/) (2023)
- [Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2404.17379/) (2024)
- [Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing](https://mlsanity.com/paper/2209.04346/) (2022)
- [A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing](https://mlsanity.com/paper/2209.15073/) (2022)
- [Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC](https://mlsanity.com/paper/2407.12887/) (2024)
- [DeepRacing: Parameterized Trajectories for Autonomous Racing](https://mlsanity.com/paper/2005.05178/) (2020)
- [Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning](https://mlsanity.com/paper/2406.17286/) (2024)
- [Multi-Agent Exploration of an Unknown Sparse Landmark Complex via Deep Reinforcement Learning](https://mlsanity.com/paper/2209.11794/) (2022)
- [AV4EV: Open-Source Modular Autonomous Electric Vehicle Platform for Making Mobility Research Accessible](https://mlsanity.com/paper/2312.00951/) (2023)

## Built on by (most cited first, 3 of 3)

- [Structured Agent Distillation for Large Language Model](https://mlsanity.com/paper/2505.13820/) (2025)
- [Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible Training](https://mlsanity.com/paper/2502.03822/) (2025)
- [Optimizing Active Perception for Learning Simultaneous Viewpoint Selection and Manipulation with Diffusion Policy](https://mlsanity.com/paper/2409.14615/) (2024)

## Abstract (arXiv)

In autonomous driving, end-to-end methods utilizing Imitation Learning (IL) and Reinforcement Learning (RL) are becoming more and more common. However, they do not involve explicit reasoning like classic robotics workflow and planning with horizons, resulting in strategies implicit and myopic. In this paper, we introduce a path planning method that uses Behavioral Cloning (BC) for path-tracking and Proximal Policy Optimization (PPO) for static obstacle nudging. It outputs lateral offset values to adjust the given reference waypoints and performs modified path for different controllers. Experimental results show that the algorithm can do path following that mimics the expert performance of path-tracking controllers, and avoid collision to fixed obstacles. The method makes a good attempt at planning with learning-based methods in path planning problems of autonomous driving.