Development Of In-Vehicle Collision Warning System For Intersections

Most of the current collision warning systems are mainly designed to detect imminent rear-end, lane-changing or lane departure collisions. None of them was designed to detect imminent intersection collisions, which were found to cause more fatalities and injuries than other types of collisions. One of the most important factors that lead to intersection collisions is driver’s human error and misjudgement. A main source for human errors is the insensitivity of human vision system to detect the speed and acceleration of approaching vehicles; and therefore, any algorithm for an intersection collision warning system should give consideration to the speed and acceleration of all approaching vehicles to mitigate the inadequacy in the human vision system. Moreover, when designing any collision warning system, false warnings should be minimized to avoid nuisance for drivers that might lead to the loss of the system’s reliability by potential users. This research proposed an intersection collision warning system that utilizes commercially-available detection sensors to detect approaching vehicles and measure their speeds and acceleration rates in order to estimate the time-to-collision and compare it to the time required for the turning vehicle to clear the paths of the approaching vehicles. By comparing these times, the system triggers a warning message if an imminent collision is detected. Minimum specifications for key hardware components are established for the proposed system which does not depend on specific technology. To estimate the time require to clear the paths of the approaching vehicles, statistical models were developed to estimate the perception-reaction time for the driver of the turning vehicle and the rate of acceleration selected when departing the intersection. The statistical models include regression models that were calibrated from data collected through driving simulation and more-sophisticated artificial neural network models that are based on actual data collected from a specific driver on a specific vehicle. The proposed system was validated by computer simulation to verify the accuracy of the developed algorithms and to measure the impact of different components on the functionality and reliability of the system. Final conclusions are provided along with recommendations for further research.