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.

中文翻译：

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Sentinel：一种用于智能车辆的车载系统，可减少高速公路事故期间的交通延误

本文介绍了 Sentinel，这是一种车载系统，可在高速公路事故期间指导智能车辆的变道行为，以减少拥堵和延误。Sentinel 建立在概率预测模型的基础上，该模型使用多个与交通和驾驶员相关的参数来估计使用多个车道变换到达道路上目标位置的概率。当检测到阻塞智能车辆车道的事件时，Sentinel 在到达事件点之前开始估计成功离开阻塞车道的概率，并在概率低于某个阈值时提醒车辆离开该车道。为了解 Sentinel 对交通流量和延迟的影响，将其用于美国 I-66 州际公路的四车道段的模拟案例研究 最右边的车道因事故而暂时阻塞。结果表明，根据事件持续时间、Sentinel 渗透率和交通流量，Sentinel 可以将平均延迟减少多达 37%。结合交通事件管理系统，Sentinel 可以成为减少延误和节省数十亿美元高速公路拥堵成本的宝贵资产。