Monocular Distance Estimation-based Approach using Deep Artificial Neural Network

Abstract

Those in authority are evaluating the test evaluation for threat assessments currently in place. Since people often depend on their feelings and moods, this may create inequality. Therefore, this study suggested applying deep learning for Autonomous Emergency Steering (AES) and Autonomous Emergency Braking (AEB) assessments in the safety rating protocol. The suggested method for the test in situation-based threat assessments is a monocular distance estimation-based approach. The camera's objective is to make it simple to conduct assessments using only an onboard dash camera. This study proposes a method based on a monocular distance estimation-based approach for test methodology in the situational-based threat assessments using deep learning for the AES system to complement the AEB system for active safety features. Then, the accuracy of the distance estimation models has validated with the ground truth distances from the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset. Thus, the output of this study can contribute to the methodological base for further understanding of drivers the following behaviour with a long-term goal of reducing rear-end collisions.