mlsanityData to 14 Oct 2026

Automated Parking Trajectory Generation Using Deep Reinforcement Learning

arXiv 2504.21071 ↗First posted 29 Apr 2025cs.ROcited by 4

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Autonomous parking is a key technology in modern autonomous driving systems, requiring high precision, strong adaptability, and efficiency in complex environments. This paper proposes a Deep Reinforcement Learning (DRL) framework based on the Soft Actor-Critic (SAC) algorithm to optimize autonomous parking tasks. SAC, an off-policy method with entropy regularization, is particularly well-suited for continuous action spaces, enabling fine-grained vehicle control. We model the parking task as a Markov Decision Process (MDP) and train an agent to maximize cumulative rewards while balancing exploration and exploitation through entropy maximization. The proposed system integrates multiple sensor inputs into a high-dimensional state space and leverages SAC's dual critic networks and policy network to achieve stable learning. Simulation results show that the SAC-based approach delivers high parking success rates, reduced maneuver times, and robust handling of dynamic obstacles, outperforming traditional rule-based methods and other DRL algorithms. This study demonstrates SAC's potential in autonomous parking and lays the foundation for real-world applications.

Key facts. Cited by 4 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 12 of its 18 references that are in the map. First posted on arXiv 29 Apr 2025.

Builds on

12 of 18 references.

References · 12

  1. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 56
  2. 2024WcDT: World-centric Diffusion Transformer for Traffic Scene Generationcited by 32
  3. 2024NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoisingcited by 30
  4. 2024CAR: Controllable Autoregressive Modeling for Visual Generationcited by 30
  5. 2024Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learningcited by 20
  6. 2024Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Modelscited by 15
  7. 2024Application of an ANN and LSTM-based Ensemble Model for Stock Market Predictioncited by 13
  8. 2025CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inferencecited by 12
  9. 2025Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Accelerationcited by 10
  10. 2024GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Processcited by 9
  11. 2022Semi-supervised multiscale dual-encoding method for faulty traffic data detectioncited by 3
  12. 2025Industrial Internet Robot Collaboration System and Edge Computing Optimizationcited by 3

Built on by

Cited by year

Citing papers per year: 2025: 4.

4 papers.

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  2. 2025Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networkscited by 6
  3. 2025Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systemscited by 6
  4. 2025Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systemscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
  2. 2026REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
  3. 2026Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
  4. 2025Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods
  5. 2024HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios
  6. 2024Speeding Up Path Planning via Reinforcement Learning in MCTS for Automated Parking
  7. 2026N3P: Accelerated Automated Parking via a Learning-Based Naturalistic Three-Stage Scheme
  8. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey