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The connected and automated car-following model can provide a model reference for the queue control algorithm of connected and automated driving and has become a hot research topic in the field of connected vehicles and intelligent transportation. A queue of fast-moving vehicles on urban roads can cause traffic congestion when forced to slow down and, in serious cases, can cause rear-impact accidents. Therefore, this paper introduces information on the time delay of information reception and processing, a collision risk quantification factor reflecting the speed characteristics of the front vehicle, and the speed limit and proposes an improved intelligent driver collision quantification model that considers drastic changes in the speed of the front vehicle. Additionally, the model parameters are calibrated using real vehicle data from urban roads combined with an improved salp swarm algorithm. Finally, the evolution rule of disturbance in the traffic flow under different states is analyzed using a time-space diagram, and the DIDM-CSCL model is compared with the classical IDM. The results show that the improved IDM can better describe the following behavior at the microscopic level, which provides a basis for research related to connected and automated driving., Author(s): Wenbo Wang [1,2]; Fei Hui (corresponding author) [1,2]; Kaiwang Zhang [1]; Xiangmo Zhao [1,2]; Asad J. Khattak [1,3] 1. Introduction Following behavior is a result of the interaction of [...]

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV's trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867kWh and 0.0532kWh, respectively. T-eco-driving mode reduces 2.5s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits., Author(s): Lan Yang [1]; Mengjie Han (corresponding author) [1]; Shan Fang [1]; Guoyuan Wu [2]; He Sheng [1]; Heng Wei [3]; Xiangmo Zhao [1] 1. Introduction EVs have been recognized [...]

In vehicle visual navigation, image matching algorithm is highly critical to positioning accuracy and processing efficiency. One single matching algorithm cannot satisfy all types of image features accurate acquisition, so Harris, SUSAN, FAST, SIFT, and SURF are respectively adopted to process various road images under normal lighting condition. During practical application, the appropriate algorithm can be selected based on detection rate and running time of the above algorithms. Aiming at the illumination change interference of the collected images in vehicle visual navigation, many traditional matching algorithms for illumination change are not optimal, so an image precise matching algorithm with illumination change robustness is proposed. Because image edges and detail information have lower sensitivity for illumination change, SURF feature points are optimized by image gradient based on the idea of Canny, and the bidirectional search is used to obtain precise matching points. The experimental results show that feature point detection of the algorithm remains good stability for illumination change in images, and the matching accuracy can reach more than 94%. The algorithm is not only robust to illumination change, but also ensures higher matching speed and meanwhile improves the matching accuracy significantly.

Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs' overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors' physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction. [ABSTRACT FROM AUTHOR]