# Computer vision in automated parking systems: Design, implementation and challenges

arXiv: https://arxiv.org/abs/2104.12537 · HTML: https://mlsanity.com/paper/2104.12537/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [DeMoN: Depth and Motion Network for Learning Monocular Stereo](https://mlsanity.com/paper/1612.02401/) (2016)

## Built on by (most cited first, 9 of 9)

- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2105.12763/) (2021)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Exploiting the Distortion-Semantic Interaction in Fisheye Data](https://mlsanity.com/paper/2305.00079/) (2023)
- [Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges](https://mlsanity.com/paper/2402.12041/) (2024)
- [Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View](https://mlsanity.com/paper/2402.00637/) (2024)
- [2.5D Vehicle Odometry Estimation](https://mlsanity.com/paper/2111.08398/) (2021)
- [Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios](https://mlsanity.com/paper/2410.03774/) (2024)

## Abstract (arXiv)

Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors' knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.