# Automated Parking Planning with Vision-Based BEV Approach

arXiv: https://arxiv.org/abs/2406.15430 · HTML: https://mlsanity.com/paper/2406.15430/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 16 of 16)

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- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving](https://mlsanity.com/paper/2303.09551/) (2023)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Optimization-Based Collision Avoidance](https://mlsanity.com/paper/1711.03449/) (2017)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Learning Directed Locomotion in Modular Robots with Evolvable Morphologies](https://mlsanity.com/paper/2001.07804/) (2020)
- [Learning Locomotion Skills in Evolvable Robots](https://mlsanity.com/paper/2010.09531/) (2020)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [Class Binarization to NeuroEvolution for Multiclass Classification](https://mlsanity.com/paper/2308.13876/) (2023)
- [End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023](https://mlsanity.com/paper/2401.08658/) (2023)
- [A Virtual Reality Training System for Automotive Engines Assembly and Disassembly](https://mlsanity.com/paper/2311.02108/) (2023)
- [The Key Artificial Intelligence Technologies in Early Childhood Education: A Review](https://mlsanity.com/paper/2401.05403/) (2023)
- [Exploration of the Assessment for AVP Algorithm Training in Underground Parking Garages Simulation Scenario](https://mlsanity.com/paper/2311.08410/) (2023)

## Abstract (arXiv)

Automated Valet Parking (AVP) is a crucial component of advanced autonomous driving systems, focusing on the endpoint task within the "human-vehicle interaction" process to tackle the challenges of the "last mile".The perception module of the automated parking algorithm has evolved from local perception using ultrasonic radar and global scenario precise map matching for localization to a high-level map-free Birds Eye View (BEV) perception solution.The BEV scene places higher demands on the real-time performance and safety of automated parking planning tasks. This paper proposes an improved automated parking algorithm based on the A* algorithm, integrating vehicle kinematic models, heuristic function optimization, bidirectional search, and Bezier curve optimization to enhance the computational speed and real-time capabilities of the planning algorithm.Numerical optimization methods are employed to generate the final parking trajectory, ensuring the safety of the parking path. The proposed approach is experimentally validated in the commonly used industrial CARLA-ROS joint simulation environment. Compared to traditional algorithms, this approach demonstrates reduced computation time with more challenging collision-risk test cases and improved performance in comfort metrics.