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

Abstract

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 computer vision and intelligent sensing techniques 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 cover works involving 2D and 3D (stereo vision) data fusion for both internal and external perception, as well as current trends regarding efficient model design and temporally-aware architectures. We provide a thorough discussion on the main positive and negative points of the state-of-the-art in Visual Robotic Attention, as well as share our experience and contributions in applying visual perception for external obstacle detection and tracking, and internal (driver) monitoring. The results presented should serve as a compilation of the history of visual perception for autonomous mobile robots (specifically, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles), thus providing the reader with a comprehensive basis on both the main contributions and the state-of-the-art in the field.