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

Running red lights remains a major cause of intersection crashes, injuries, and fatalities. Although numerous counter-measures have been proposed, they continue to be a major problem in practice, partly because most existing systems deliver uniform guidance to every driver, prompting some motorists to ignore or misinterpret the alerts and leaving a persistent safety gap. We present a novel method for providing accurate, individualized warnings in place of the broad, one-size-fits-all alerts used by 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 behavior; the proposed warning system contains three parts. First, a traffic prediction algorithm uses vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data to predict traffic conditions along the approach to a signalized intersection. Second, an optimization-based algorithm that computes driver-specific warning that minimizes the risk of a red light violation, given the predicted traffic states and driver reaction model. Third, the resulting advisory—quantifying the required deceleration—is presented on the in-vehicle display and updated continuously as the vehicle approaches 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 the red light running warning system (RLRWS). The results show that the proposed system provides more effective and accurate warning signals to drivers. In the simulation, the proposed algorithm cuts the ego vehicle’s peak deceleration by up to 72.2% relative to an unguided baseline, greatly reducing the risk of red light violation.