# Automated Parking Trajectory Generation Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.21071 · HTML: https://mlsanity.com/paper/2504.21071/

## 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.

## 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, 12 of 12)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [WcDT: World-centric Diffusion Transformer for Traffic Scene Generation](https://mlsanity.com/paper/2404.02082/) (2024)
- [NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising](https://mlsanity.com/paper/2403.20034/) (2024)
- [CAR: Controllable Autoregressive Modeling for Visual Generation](https://mlsanity.com/paper/2410.04671/) (2024)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [Human-centric Reward Optimization for Reinforcement Learning-based Automated Driving using Large Language Models](https://mlsanity.com/paper/2405.04135/) (2024)
- [Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction](https://mlsanity.com/paper/2410.20253/) (2024)
- [CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference](https://mlsanity.com/paper/2501.15852/) (2025)
- [Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration](https://mlsanity.com/paper/2504.00950/) (2025)
- [GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process](https://mlsanity.com/paper/2410.18448/) (2024)
- [Semi-supervised multiscale dual-encoding method for faulty traffic data detection](https://mlsanity.com/paper/2212.13596/) (2022)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)

## Built on by (most cited first, 4 of 4)

- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2506.17551/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)

## 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.