CARINA Project: Visual Perception Systems Applied for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS)

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Bruno, Diego R., Berri, Rafael, Barbosa, Felipe, Osorio, Fernando S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accidents; Advanced driver assistance systems; Automation; Autonomous vehicles; Computer Vision; Data integration; Deep Learning; Mobile computing; Monitoring; Navigation; Obstacle avoidance; Obstacle detection and classification; Roads; Robotic vehicles; Safety; State of the art; Tracking; Vehicles; Vision systems; Visual perception
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3287491

Autonomous mobile robots use computational techniques of great complexity so that to allow navigation in various types of dynamic environments, avoiding collisions with obstacles and always seeking to optimize the best route, ultimately enabling them to operate in a safe and precise manner. In order for navigation at this level to be possible, a variety of intelligent sensing techniques and computer vision are used. The potential of an intelligent computer vision system to detect and predict the actions of dynamic agents on the streets is applied to increase traffic safety with intelligent robotic vehicles. In this paper we present a systematic review of computer vision models for the detection and tracking of obstacles in traffic environments. Specifically, we will cover works involving 2D and 3D data fusion for both internal and external perception, as well as current trends regarding efficient model design and temporally-aware architectures. Alongside our review, we also provide a thorough discussion on the main positive and negative points of the state-of-the-art for detecting and tracking obstacles in Visual Robotic Attention works, as well as share our experience in applying visual perception for external obstacle detection and tracking, as well as internal (driver) monitoring. The results presented should serve as a compilation of the history of visual perception for autonomous mobile robots, and our contributions to the field, thus enabling an advance for research in the area of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles.