Application of Machine Vision to Vehicle Automatic Collision Warning Algorithm

Using the new technologies such as information technology, communication technology and electronic control technology, vehicle collision warning system（CWS） can acquire road condition, adjacent vehicle march condition as well as its dynamics performance continuously, then it can forecast the oncoming potential collision and give a warning. Based on the analysis of driver＇s driving behavior, algorithm＇s warning norms are determined. Based on warning norms adopting machine vision method, the cooperation collision warning algorithm（CWA） model with multi-input and multi-output is established which is used in supporting vehicle CWS. The CWA is tested using the actual data and the result shows that this algorithm can identify and carry out warning for vehicle collision efficiently, which has important meaning for improving the vehicle travel safety.

Developing Framework to Test Driving Performance at Left-Turn Movement with In-Vehicle Advance Collision Warning Message

Left-turning traffic without a protected left-turn signal is one of the major safety concerns at urban intersections. Though an average of only l0% - 15% of all approaching traffic turns left, significantly a large proportion of left-turn crashes occur involving 21% of all intersection fatal crashes. Where traditional safety countermeasures of signal timing-phasing and use of flashing yellow light have reportedly failed to significantly reduce the rate of crashes, an in-vehicle advance collision warning message can be helpful to reduce left-turn collisions at intersections. In this study, an in-vehicle audio warning application has been designed by providing two safety warning messages (Advance Warning Message and Safe Left-turn Maneuver Message) under the vehicle to vehicle (V2V) communication system, which is triggered based on the acceptable gaps of oncoming opposing vehicles for a safe left-turn. A driving simulator test has been conducted with 30 participants to investigate the impacts of warning messages on performance measures such as speed and acceleration profiles, collision records, brake reaction distance, and intersection clearance time. Statistical results showed that with the help of these messages, all participants were able to reduce speeds and accelerations and chose suitable gaps without potential conflicts. Moreover, the results of questionnaire analysis provide a positive acceptability especially for the Safe Left-turn Maneuver Message. Based on the performance measurements, this type of safety warning messages can be recommended for possible real-road tests for practical applications.

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.