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Anticipatory Lane Change Warning using Vehicle-to-Vehicle Communications

机译:使用车对车通信的预期车道变更警告

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Conventional lane change warning and automated lane changing systems detect other vehicles using on-board sensors such as camera, radar, and ultrasonic sensors. With the advent of Connected Vehicle (CV) technology, wireless communication (e.g, Dedicated Short Range Communications, or DSRC) becomes another option for “sensing” surrounding vehicles. In particular, DSRC does not have the line-of-sight limitation of ranging sensors and thus can “see” traffic farther ahead, which lends itself well to anticipating the movements of nearby vehicles. We have developed an algorithm that uses such data to predict whether a desired lane change will result in an unsafe situation, and prevents the lane change if that is the case. The effectiveness was evaluated in the microscopic traffic simulator VISSIM using a freeway network that has been well calibrated with rush hour traffic data. System performance in terms of safety was estimated using the Surrogate Safety Assessment Model (SSAM) under a variety of traffic scenarios (different congestion levels, penetration rates of connected vehicles and application-equipped vehicles). Preliminary tests showed that the proposed algorithm can reduce the number of potential traffic conflicts by up to 30%, with higher reductions at higher traffic volumes and higher percentages of application-equipped vehicles.
机译:传统的车道变更警告和自动车道变更系统会使用摄像机,雷达和超声波传感器等车载传感器来检测其他车辆。随着互联车辆(CV)技术的出现,无线通信(例如,专用短程通信或DSRC)成为“感知”周围车辆的另一种选择。特别是,DSRC没有测距传感器的视线限制,因此可以“看到”更远的交通,这很容易预测附近车辆的运动。我们已经开发出一种算法,使用这种数据来预测所需的车道变更是否会导致不安全的情况,并在这种情况下防止车道变更。有效性在微观交通模拟器VISSIM中使用高速公路网络进行了评估,该高速公路网络已对高峰时间交通数据进行了很好的校准。使用替代安全评估模型(SSAM)在各种交通场景(不同的拥堵程度,联网车辆和配备应用程序的车辆的渗透率)下估算系统的安全性能。初步测试表明,所提出的算法可以将潜在的交通冲突数量减少多达30%,并且在交通量增加和配备应用程序的车辆所占百分比较高的情况下,交通冲突的发生率会更高。

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