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Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model's precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future. [ABSTRACT FROM AUTHOR]

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value. [ABSTRACT FROM AUTHOR]

Motivated by the low efficiency and poor accuracy of the existing techniques for measuring the depth of a pavement structure, we examine the characteristics of laser speckle and speckle measurement methods in relation to the depth of a pavement structure and propose a set of methods for the 3D measurement of such structures based on laser speckle and digital image processing. First, we analyze the relevant characteristics of laser speckle measurement, and then based on the relevant region and temporal sequence correlation, a faster digital speckle correlation method is used to establish a mathematical relationship between the 3D image and the structural depth. The erosion and dilation technique and mean optimization algorithm are proposed to eliminate the noise and burr of primeval 3D reconstruction. After this, a 3D point cloud of information on the depth of the pavement structure is obtained. Finally, through fitting, a good linear relationship was found between the texture depth obtained by the sand paving method and the system. From measurements using a laboratory calibration platform, it was found that the accuracy of measurement of our method can reach the subpixel level, and the running speed is high, meaning that it can be used for highly efficient and accurate detection of the depth of a road structure. [ABSTRACT FROM AUTHOR]