论文标题
一种概率的方法，用于驾驶员协助以减少拥挤的高速公路车道的延迟减少
A Probabilistic Approach to Driver Assistance for Delay Reduction at Congested Highway Lane Drops
论文作者
论文摘要
本文提出了一个基于概率预测模型的机载预先警告系统，该预测模型建议车辆何时更改车道以进行即将到来的车道下降。使用几个与驾驶员和驱动因素相关的参数，例如车间前进距离的分布，预测模型可以计算使用一个或多个车道更改以成功达到道路上的目标位置的可能性。当接近车道下降时，车载系统将当前的车辆条件投射到将来，并使用该模型在到达车道之前不断估算更换车道的成功概率，并建议驾驶员或自动驾驶汽车在换车机上启动车道时，当该概率下降到一定阈值以下时。在模拟案例研究中，提出的系统用于在I -81州际公路的一段，并带有两条车道液滴 - 从四个车道过渡到两个车道 - 建议车辆避免车道滴。结果表明，根据交通流量和配备预先警告系统的车辆比率，提议的系统可以将平均延迟降低高达50％，最大延迟最多减少33％。
This paper proposes an onboard advance warning system based on a probabilistic prediction model that advises vehicles on when to change lanes for an upcoming lane drop. Using several traffic- and driver-related parameters such as the distribution of inter-vehicle headway distances, the prediction model calculates the likelihood of utilizing one or multiple lane changes to successfully reach a target position on the road. When approaching a lane drop, the onboard system projects current vehicle conditions into the future and uses the model to continuously estimate the success probability of changing lanes before reaching the lane-end, and advises the driver or autonomous vehicle to start a lane changing maneuver when that probability drops below a certain threshold. In a simulation case study, the proposed system was used on a segment of the I-81 interstate highway with two lane drops - transitioning from four lanes to two lanes - to advise vehicles on avoiding the lane drops. The results indicate that the proposed system can reduce average delay by up to 50% and maximum delay by up to 33%, depending on traffic flow and the ratio of vehicles equipped with the advance warning system.
