Forward collision warning system for motorcyclist using smart phone sensors based on time-to-collision and trajectory prediction

Abstract

Purpose The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle). Design/methodology/approach This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor. Findings This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification. Originality/value The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends

“…Tang and Li [ 147 ] propose ‘End-to-End Monocular Range Estimation’ for collision warning. Lim et al [ 148 ] created a ‘Forward Collision Warning System for Motorcyclists’ using smartphone sensors. Farhat, Rhaiem, Faiedh, and Souani [ 149 ] present a ‘Cooperative Forward Collision Avoidance System Based on Deep Learning’.…”

“…Tang et al [147] introduce a monocular range estimation system using a single camera for precise FCWS, especially in difficult scenarios. Lim et al [148] suggest a smartphone-based FCWS for motorcyclists utilizing phone sensors to predict collision risks. Farhat et al [149] [150] propose a 'Lightweight Collaboration of Detecting and Tracking Algorithm' for embedded systems.…”

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“…Object recognition plays a significant role in intelligent transportation systems, serving as both a crucial technical method and a fundamental link between high-level tasks such as target tracking and behavior recognition. It is an important research field within computer vision, aiming to efficiently identify vehicle targets and extract relevant feature information from static or dynamic videos using machine learning, AI, pattern recognition, image processing, and other technologies (Lim et al, 2021). Its wide application and research value can be observed in areas such as intelligent transportation, vehicle tracking, and unmanned driving, among others.…”

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