Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Computing and Information Engineering, Hubei University, Wuhan 430062, China.
Sensors (Basel). 2021 Jul 23;21(15):5003. doi: 10.3390/s21155003.
The emerging connected and automated vehicle (CAV) has the potential to improve traffic efficiency and safety. With the cooperation between vehicles and intersection, CAVs can adjust speed and form platoons to pass the intersection faster. However, perceptual errors may occur due to external conditions of vehicle sensors. Meanwhile, CAVs and conventional vehicles will coexist in the near future and imprecise perception needs to be tolerated in exchange for mobility. In this paper, we present a simulation model to capture the effect of vehicle perceptual error and time headway to the traffic performance at cooperative intersection, where the intelligent driver model (IDM) is extended by the Ornstein-Uhlenbeck process to describe the perceptual error dynamically. Then, we introduce the longitudinal control model to determine vehicle dynamics and role switching to form platoons and reduce frequent deceleration. Furthermore, to realize accurate perception and improve safety, we propose a data fusion scheme in which the Differential Global Positioning system (DGPS) data interpolates sensor data by the Kalman filter. Finally, a comprehensive study is presented on how the perceptual error and time headway affect crash, energy consumption as well as congestion at cooperative intersections in partially connected and automated traffic. The simulation results show the trade-off between the traffic efficiency and safety for which the number of accidents is reduced with larger vehicle intervals, but excessive time headway may result in low traffic efficiency and energy conversion. In addition, compared with an on-board sensor independently perception scheme, our proposed data fusion scheme improves the overall traffic flow, congestion time, and passenger comfort as well as energy efficiency under various CAV penetration rates.
新兴的互联和自动驾驶车辆 (CAV) 有潜力提高交通效率和安全性。通过车辆与交叉口之间的合作，CAV 可以调整速度并形成车队以更快地通过交叉口。然而，由于车辆传感器的外部条件，可能会出现感知错误。同时，CAV 和传统车辆将在不久的将来共存，需要容忍不精确的感知以换取机动性。在本文中，我们提出了一个仿真模型来捕捉车辆感知错误和时距对协作交叉口交通性能的影响，其中智能驾驶员模型 (IDM) 通过 Ornstein-Uhlenbeck 过程扩展来动态描述感知错误。然后，我们引入了纵向控制模型来确定车辆动力学和角色切换，以形成车队并减少频繁的减速。此外，为了实现准确的感知和提高安全性，我们提出了一种数据融合方案，其中差分全球定位系统 (DGPS) 数据通过卡尔曼滤波器对传感器数据进行插值。最后，全面研究了感知错误和时距如何影响部分互联和自动化交通中协作交叉口的碰撞、能耗和拥堵。仿真结果表明了交通效率和安全性之间的权衡，即随着车辆间隔的增大，事故数量减少，但过大的时距可能导致低交通效率和能量转换。此外，与车载传感器独立感知方案相比，我们提出的数据融合方案在各种 CAV 渗透率下提高了整体交通流量、拥堵时间、乘客舒适度和能源效率。
