Robotic Visual Attention Architecture for ADAS in Critical Embedded Systems for Smart Vehicles
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This paper presents the development of a perception architecture for Advanced Driver Assistance Systems (ADAS) capable of integrating (a) external and (b) internal vehicle perception to evaluate obstacles, traffic signs, pedestrians, navigable areas, potholes and deformations in road, as well as monitor driver behavior, respectively. For external perception, in previous works we used advanced sensors, such as the Velodyne LIDAR-64, the Bumblebee 3D camera for object depth analysis, but in this work, focusing on reducing hardware, processing and time costs, we apply 2D cameras with depth estimation generated by the Depth-Anything V2 network model. Internal perception is performed using the Kinect v2 and the Jetson Nano in conjunction with a SVM (Support Vector Machine) model, allowing the identification of driver posture characteristics and the detection of signs of drunkenness, drowsiness or disrespect for traffic laws. The motivation for this system lies in the fact that more than 90% of traffic accidents in Brazil are caused by human error, while only 1% are detected by surveillance means. The proposed system offers an innovative solution to reduce these rates, integrating cutting-edge technologies to provide advanced road safety. This perception architecture for ADAS offers a solution for road safety, alerting the driver and allowing corrective actions to prevent accidents. The tests carried out demonstrated an accuracy of more than 92% for external and internal perception, validating the effectiveness of the proposed approach.
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ADAS, Autonomous Vehicles, Computer Vision, Driver Assistance, Machine Learning
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Inglês
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 2, p. 871-878.
