mlsanityData to 14 Oct 2026

SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning

arXiv 2509.13956 ↗First posted 17 Sep 2025cs.ROcited by 1

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Autonomous parking is a critical component for achieving safe and efficient urban autonomous driving. However, unstructured environments and dynamic interactions pose significant challenges to autonomous parking tasks. To address this problem, we propose SEG-Parking, a novel end-to-end offline reinforcement learning (RL) framework to achieve interaction-aware autonomous parking. Notably, a specialized parking dataset is constructed for parking scenarios, which include those without interference from the opposite vehicle (OV) and complex ones involving interactions with the OV. Based on this dataset, a goal-conditioned state encoder is pretrained to map the fused perception information into the latent space. Then, an offline RL policy is optimized with a conservative regularizer that penalizes out-of-distribution actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. Comparative results demonstrate the superior performance of our framework with the highest success rate and robust generalization to out-of-distribution parking scenarios. The related dataset and source code will be made publicly available after the paper is accepted.

Key facts. Cited by 1 paper 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 22 of its 42 references that are in the map. First posted on arXiv 17 Sep 2025.

Builds on

22 of 42 references.

Closest ancestors · 1

Same direction, newest first

  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey2 Feb

Other references · 21

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  4. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 490
  6. 2017End-to-end Driving via Conditional Imitation Learningcited by 485
  7. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 302
  8. 2019Causal Confusion in Imitation Learningcited by 288
  9. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  10. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  11. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 161
  12. 2022When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?cited by 89
9 more
  1. 2021Regularized Behavior Value Estimationcited by 32
  2. 2022Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopescited by 26
  3. 2024ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planningcited by 15
  4. 2022Behavior Prior Representation learning for Offline Reinforcement Learningcited by 12
  5. 2024HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarioscited by 11
  6. 2024Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersectionscited by 10
  7. 2022Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planningcited by 5
  8. 2025Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving at Unsignalized Intersectionscited by 4
  9. 2018Improving Trajectory Optimization using a Roadmap Frameworkcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfercited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations
  2. 2026ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation
  3. 2025Automated Parking Trajectory Generation Using Deep Reinforcement Learning
  4. 2026N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
  5. 2025Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods
  6. 2026NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments
  7. 2025A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking
  8. 2023iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning