A Traffic Prediction-Based Individualized Driver Warning System to Reduce Red Light Violations

TL;DR

This paper introduces a personalized traffic warning system that predicts traffic conditions and driver behavior to provide tailored red light violation warnings, aiming to improve safety and reduce accidents.

Contribution

It presents a novel integrated system combining traffic prediction, warning optimization, and driver display to deliver individualized alerts for red light violations.

Findings

More effective warning signals compared to previous systems

Improved accuracy in predicting red light violations

Successful validation through simulations and real-world tests

Abstract

Red light violation is a major cause of traffic collisions and resulting injuries and fatalities. Despite extensive prior work to reduce red light violations, they continue to be a major problem in practice, partly because existing systems suffer from the flaw of providing the same guidance to all drivers. As a result, some violations are avoided, but other drivers ignore or respond inappropriately to red light running systems, resulting in safety issues overall. We show a method of providing accurate warnings to individual drivers to avoid the broad guidance approach of most existing systems. Recognizing if a driver will run red lights is highly dependent on signal phase and timing, traffic conditions along the road, and individual driver behaviour, the proposed warning system contains three parts: a traffic prediction algorithm, an individual warning signal optimizer, and a driver warning display. The traffic prediction algorithm predicts future traffic states along the road towards the signalized intersections using the latest traffic conditions obtained through vehicle-to-vehicle and vehicle-to-infrastructure communications. Then, an optimization problem is formulated to compute the optimal warning signal based on predicted traffic states and driver reaction model. Finally, the optimal warning signal is shown on the display screen to advise driver on how much braking is needed to avoid running the red light. The system continuously updates the latest warning signal as the vehicle is approaching the intersection. Both numerical simulated driving scenarios and real-world road tests are used to demonstrate the proposed algorithm's performance under different conditions by comparing with previous work on red light running warning system. The results show that the system provides more effective and accurate warning signals to drivers, helping them avoid running red lights.