Sentinel: An Onboard System for Intelligent Vehicles to Reduce Traffic Delay during Freeway Incidents

This paper introduces Sentinel, an onboard system that guides the lane change behavior of intelligent vehicles during a freeway incident to reduce congestion and delay. Sentinel is built upon a probabilistic prediction model that uses several traffic- and driver-related parameters to estimate the probability of reaching a target position on the road using a number of lane changes. When an incident blocking the lane of an intelligent vehicle is detected, Sentinel starts estimating the probability of successfully departing the blocked lane before reaching the point of incident and alerts the vehicle to depart that lane when the probability drops below a certain threshold. To understand the impact of Sentinel on traffic flow and delay, it is used in a simulation case study of a four-lane segment of the I-66 interstate highway in the U.S. where the rightmost lane is temporarily blocked due to an incident. The results show that Sentinel can reduce average delay by up to 37%, depending on incident duration, Sentinel penetration rate, and traffic flow. In combination with Traffic Incident Management systems, Sentinel can be a valuable asset in reducing delay and saving billions of dollars in the cost of congestion on freeways.