# RL-OGM-Parking: Lidar OGM-Based Hybrid Reinforcement Learning Planner for Autonomous Parking

arXiv: https://arxiv.org/abs/2502.18846 · HTML: https://mlsanity.com/paper/2502.18846/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Placed in

- 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, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https://mlsanity.com/paper/1709.07857/) (2017)
- [ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning](https://mlsanity.com/paper/2408.02061/) (2024)
- [HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios](https://mlsanity.com/paper/2405.20579/) (2024)

## Built on by (most cited first, 5 of 5)

- [ParkFormer: A Transformer-Based Parking Policy with Goal Embedding and Pedestrian-Aware Control](https://mlsanity.com/paper/2506.16856/) (2025)
- [TRACE: A Self-Improving Framework for Robot Behavior Forecasting with Vision-Language Models](https://mlsanity.com/paper/2503.00761/) (2025)
- [A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking](https://mlsanity.com/paper/2510.14000/) (2025)
- [REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer](https://mlsanity.com/paper/2605.08713/) (2026)
- [NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments](https://mlsanity.com/paper/2608.24485/) (2026)

## Abstract (arXiv)

Autonomous parking has become a critical application in automatic driving research and development. Parking operations often suffer from limited space and complex environments, requiring accurate perception and precise maneuvering. Traditional rule-based parking algorithms struggle to adapt to diverse and unpredictable conditions, while learning-based algorithms lack consistent and stable performance in various scenarios. Therefore, a hybrid approach is necessary that combines the stability of rule-based methods and the generalizability of learning-based methods. Recently, reinforcement learning (RL) based policy has shown robust capability in planning tasks. However, the simulation-to-reality (sim-to-real) transfer gap seriously blocks the real-world deployment. To address these problems, we employ a hybrid policy, consisting of a rule-based Reeds-Shepp (RS) planner and a learning-based reinforcement learning (RL) planner. A real-time LiDAR-based Occupancy Grid Map (OGM) representation is adopted to bridge the sim-to-real gap, leading the hybrid policy can be applied to real-world systems seamlessly. We conducted extensive experiments both in the simulation environment and real-world scenarios, and the result demonstrates that the proposed method outperforms pure rule-based and learning-based methods. The real-world experiment further validates the feasibility and efficiency of the proposed method.