Anomaly Management: Reducing the Impact of Anomalous Drivers with Connected Vehicles

Anomalous drivers with errorable behaviors result in dangerous driving environments on roads, and they significantly increase risk of vehicle collisions for themselves and their surrounding vehicles. Eliminating the impact of anomalous drivers to the surrounding vehicles is very critical to improve driving safety. In this paper, an anomaly management system is developed with the help of connected vehicles to solve the problem. An errorable car-following model is introduced to model the dynamics of anomalous vehicles and to analyze their impacts to other vehicles. The system utilizes connected vehicles to monitor the errorable behaviors of the anomaly drivers and estimates acceleration and lane changing advice for connected vehicles to avoid dangerous behaviors. The anomaly management system is evaluated with both synthetic experiments and microscopic traffic simulations to understand its benefits on mitigating the risk of vehicle collisions. In the synthetic experiments, the proposed system shows its capability of removing collision and near-collision events completely. The microscopic simulation indicates that the system can reduce the probability of collisions by up to 10% and the ratio of time to collision by 22%.