A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles

Abstract

The main functions of the automated systems rely on the advanced sensors for detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect the potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in the extreme weather conditions such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, a triple radar arrangement (long-range, medium-range, and short-range radars) based on sensor fusion technique is proposed to detect objects with different size in level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including truck, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.

Radar Based Pedestrian Classification using Deep Learning Approach

“…When there are no other items within a 0.38-meter radius of the LiDAR, the algorithm can detect a single object as far away as 21.54 meters. Javad Enayati [17] suggested using a sensor fusion approach to deploy a tri-radar setup (long range, medium range, and short range) to spot objects of varying sizes when driving with advanced driver-assistance systems. All the usual suspects, such as cars, people, and animals, can be spotted in various contexts.…”