Vehicle boundary improvement and passing vehicle detection in driver assistance by flow distribution

Research in advanced driver assistance system (ADAS) is an important step towards achieving the goal of autonomous intelligent vehicle. Vehicle detection and its distance estimation is an important solution of ADAS for forward collision warning applications. Partial occlusions of passing vehicles makes their detections tedious yet the accuracy of vehicle detection in all its forms in the scene and their corresponding distance estimation is a vital factor to deploy the solution. A small deviation in detection and distance accuracy could end up in a greater mishap in ADAS and AV (Autonomous Vehicle). The proposed framework addresses the aforementioned problems of detection of passing vehicles and perfecting distance measurement by accurate lower bound estimation through Inter and Intra-Frame Flow Correspondence (I 2 F 2 C). The proposed generic framework of 1 2 F 2 C could be employed as a plug-in for the existing machine learning (ML) [1]/ deep learning (DL) [2] based algorithms for improving accuracy of distance estimation of vehicles and also improve accuracy and performance of passing vehicle detection with a detailed mathematical model of motion confidence.