Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 22 June 2022 / Approved: 23 June 2022 / Online: 23 June 2022 (10:36:09 CEST)

How to cite: Enayati, J.; Asef, P.; Jonnalagadda, Y. A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles. Preprints 2022, 2022060323 (doi: 10.20944/preprints202206.0323.v1). Enayati, J.; Asef, P.; Jonnalagadda, Y. A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles. Preprints 2022, 2022060323 (doi: 10.20944/preprints202206.0323.v1).

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.

Keywords

Autonomous vehicles, triple radar, Level 2 ADAS system, lane keeping assistance, nonlinear model predictive controller, safety, autonomous emergency braking.

Subject

ENGINEERING, Electrical & Electronic Engineering

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