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

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