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

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 Kalmanfilter 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 lastfinding 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).

Evaluating the moderating effect of in-vehicle warning information on mental workload and collision avoidance performance

Purpose–The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers.Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload.However,whether moderation and mediation effects exist among warning information,driver’s cognition,behavior and risky avoidance performance is unclear.Design/methodology/approach–This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior.A driving simulator experiment was conducted with waring and command information.Findings–Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario.The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk.At the same time,command information can weaken this negative effect.Moreover,improved collision avoidance behavior can be achieved through increasing drivers’mental workload.Practical implications–The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior,thus contributing to improved in-vehicle information system design.Originality/value–The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’collision avoidance ability.Through further calibration with larger sample size,the proposed structural model can be used to predict the effect of invehicle warnings in different risky driving scenarios.

Evaluating effectiveness and acceptance of advanced driving assistance systems using field operational test

A large number of reported road collisions are caused by driver inattention,and inappropriate driving behaviour.This study investigated the effectiveness and acceptance of Advanced Driving Assistance Systems(ADAS)for driver age groups,gender,occupation(professional/non-professional),and road type(expressway,urban roads,and semi-urban road)based on the Field Operational Test(FOT).The ADAS is provided with assistance features,such as Lane Departure Warning(LDW),Forward Collision Warning(FCW),and Traffic Speed Recognition Warning(TSRW).In total,the FOT involved 30 participants who drove the test vehicle twice(once in the stealth phase and once in the active phase).The FOT included three sections:expressway(20.60 km),urban road(7.2 km),and semi-urban road(13.35 km).A questionnaire was used to determine user acceptance of the ADAS technology.In addition,parametric and non-parametric statistical tests were carried out to determine ADAS's significant effects.The FOT results showed statistically significant differences in the LDW’s acceptance and effectiveness for gender,age group,occupation,and road type before and after exposure to ADAS.Male participants showed significant lateral behavior improvement compared to female participants.Old-aged drivers scored the highest acceptance score for the technology compared to middle and young-aged drivers.The subjective ratings ranked the assistance features in descending order as TSRW,LDW,and FCW.This study’s findings can support policy development and induce trust in the public for the technology adoption to improve road traffic safety.