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

By Markus Heimberger, Jonathan Horgan, Ciarán Hughes, John McDonald Jr., Senthil Kumar Yogamani. 2017

122 citations recorded by the source corpus.

Abstract 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.

Subfields: Aerospace Engineering, Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition, Robotics & Control

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “Fisheye is particularly beneficial to automotive low speed manoeuvring scenarios such as parking [21] where accurate full coverage near field sensing can be achieved with just four cameras.”
NeurAll: Towards a Unified Model for Visual Perception in Automated Driving (2019, unknown): “This application is quite mature and has already been deployed in previous generation vehicles for driver assistance and automated driving applications [1].”
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving (2019, background): “a robot to navigate, there has to be robust classification to determine the type of each object for planning the interaction, especially for complex scenarios like parking [9] [11].”
Artificial intelligence applications in the development of autonomous vehicles: a survey (2020, methodology): “Forty-six technical papers introduced DL approaches including convolutional neural networks (CNN), LSTM, and deep belief neural networks (DBN) to applications such as vehicle perception, automatic parking, and direct decision making.”
NeurAll: Towards a Unified Visual Perception Model for Automated Driving (2019, unknown): “This application is quite mature and has already been deployed in previous generation vehicles for driver assistance and automated driving applications [1].”
SoilingNet: Soiling Detection on Automotive Surround-View Cameras (2019, background): “The first generation systems primarily used a single camera and more recently more cameras are used to get full coverage around the vehicle to handle more complex driving scenarios [5].”