Variable speed limit (VSL) can be used on freeways to manage traffic flow with the goal of improving capacity. To achieve this objective, it is necessary that both speed and density dynamics be represented accurately. In this study, to deeply understand the effectiveness of VSL control, an analytical model was developed to represent drivers’ response to updated speed limits and macroscopic speed dynamical change with respect to changeable speed limits. Specifically, to model the freeway links having VSL control, the fundamental diagram (FD) was replaced with the VSL control variable in the relaxation term of the METANET. This modification led to the speed control variable appearing linearly, which is preferable for online computation. The density dynamics are based on the cell transmission model (CTM), which is introduced to estimate the transition flow among successive links with some practical constraints. It also offers flexibility in designing active bottleneck in which there is a capacity dro...

The integration of cloud computing and edge computing is an effective way to achieve global consistent and real-time multi-robot Simultaneous Localization and Mapping (SLAM). Cloud computing effectively solves the problem of limited computing, communication and storage capacity of terminal equipment. However, limited bandwidth and extremely long communication links between terminal devices and the cloud result in serious performance degradation of multi-robot SLAM systems. To reduce the computational cost of feature tracking and improve the real-time performance of the robot, a lightweight SLAM method of optical flow tracking based on pyramid IMU prediction is proposed. On this basis, a centralized multi-robot SLAM system based on a robot-edge-cloud layered architecture is proposed to realize real-time collaborative SLAM. It avoids the problems of limited on-board computing resources and low execution efficiency of single robot. In this framework, only the feature points and keyframe descriptors are transmitted and lossless encoding and compression are carried out to realize real-time remote information transmission with limited bandwidth resources. This design reduces the actual bandwidth occupied in the process of data transmission, and does not cause the loss of SLAM accuracy caused by data compression. Through experimental verification on the EuRoC dataset, compared with the current most advanced local feature compression method, our method can achieve lower data volume feature transmission, and compared with the current advanced centralized multi-robot SLAM scheme, it can achieve the same or better positioning accuracy under low computational load. Note to Practitioners —The purpose of this paper is to reduce the communication load of a Cloud-Edge-Robot system by compressing and transmitting of keyframes and non-keyframes, respectively, which is suitable for a multi-robot SLAM system and can realize multi-robot joint localization and sparse map reconstruction under efficient communication. Currently, remote SLAM or centralized multi-robot SLAM is usually implemented by transferring the whole image or the features and descriptors of the image. In this paper, lightweight SLAM optical flow tracking based on pyramid IMU prediction is implemented to track non-keyframes. At the edge server, tracking between non-keyframes is realized only by transmitting keypoints. For keyframes, the pose estimation is realized by transmitting compressed features and descriptors. Multi-robot localization and map fusion are realized in the cloud through key frame feature information. Experiments on public datasets show that this method is feasible and can achieve high-precision joint positioning with a low amount of transmitted data. In future studies, we will apply this framework to more real-world systems, while achieving rich, accurate map fusion with more advanced features.

Due to the rising cost of human resources in road maintenance and the pursuit of efficiency, autonomous road inspection vehicles are developed for intelligent detection of road disease to prevent severe traffic disasters in the early stages. Nevertheless, as a prevalent road disease, road cracks are diverse and susceptible to shadows, weather changes, and noise in data acquisition. Moreover, they usually appear with thin shapes that are hard to detect correctly by existing methods. To handle this problem, more details of the road cracks need to be better analyzed. In this article, we propose a refined road crack detection method named locally enhanced cross-shaped windows transformer (LECSFormer), which adopts a delicate design of the encoder-decoder structure. The encoder employs window-based transformer blocks to model long-range dependencies. Each transformer block ensembles the locally enhanced module to enrich the local contextual information, and token shuffle operation is applied to build cross-windows connections. The decoder uses dense connections to fuse multi-scale information, and the feature fusion module fuses hierarchical features and reweights them by the channel attention mechanism. The proposed method outperforms other state-of-the-art methods with ODS of 0.963, 0.917, 0.952, and 0.953 on four challenging datasets, CrackTree260, CrackLS315, Stone331, and CRKWH100. It can accurately detect cracks in road surfaces and support intelligent preventive maintenance of roads.

Microscopic detail of complex vehicle interactions in mixed traffic, involving manual driving system (MDS) and automated driving system (ADS), is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle (CAV) environment. In this context, this paper proposes a naïve microscopic car-following strategy for a mixed traffic stream in CAV settings and specified shifts in traffic mobility, safety, and environmental features. Additionally, this study explores the influences of platoon properties (i.e., intra-platoon headway, inter-platoon headway, and maximum platoon length) on traffic stream characteristics. Different combinations of MDS and ADS vehicles are simulated in order to understand the variations of improvements induced by ADS vehicles in a traffic stream. Simulation results reveal that grouping ADS vehicles at the front of traffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximum mobility benefits for upstream vehicles. Both mobility and environmental improvements can be realized by forming long, closely spaced ADS vehicles at the cost of reduced safety. To achieve balanced mobility, safety, and environmental advantages from mixed traffic environment, dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS market penetrations.

Shortening inter-vehicle distance can increase traffic throughput on roads for increasing volume of vehicles. In the process, traffic accidents occur more frequently, especially for multi-car accidents. Furthermore, it is difficult for drivers to drive safely under such complex driving conditions. This article investigates multi-vehicle longitudinal collision avoidance issue under such traffic conditions based on the Advanced Emergency Braking System (AEBS). AEBS is used to avoid collisions or mitigate the impact during critical situations by applying brake automatically. Hierarchical multi-vehicle longitudinal collision avoidance controller is proposed to guarantee safety of multi-cars using Vehicle-to-Infrastructure (V2I) communication capability in addition to radar for longitudinal vehicle control. High-level controller is designed to ensure safety of multi-cars and optimize total energy by calculating the target braking force. Vehicle network is used to get the key vehicle-road interaction data and constrained hybrid genetic algorithm (CHGA) is adopted to decouple the vehicle-road interactive system,which can obtain the maximum ground friction through vehicle-road data, and provide key predictive parameters for multi-vehicle safety controller. Lower level non-singular Fractional Terminal Sliding Mode(NFTSM) Controller is built to achieve control goals of high-level controller. Simulations are carried out under typical driving conditions. Results verify that the proposed system in this article can avoid or mitigate the collision risk compared to the vehicle without this system.

Compared to vehicle‐only collisions, traffic collisions with pedestrians are studied less because of insufficient data. However, with the development of image processing technology, a growing number of road user behavioural analyses have been conducted using video data. This study tries to extract road users’ movements from video data in order to analyse the conflict between pedestrian and vehicle and to evaluate pedestrian safety performance during conflicts. The time difference to collision (TDTC) parameter is used to fit the safety analysis on pedestrian‐involved conflicts. Scene‐based analysis, which evaluates the safety performance of traffic intersections and segments where several pedestrian–vehicle conflicts may happen together, is conducted using 91 groups of scene data. The parameters most related to pedestrian safety are located using a sensitivity test, a quantitative definition of pedestrian–vehicle conflict is then defined, and a scene‐based pedestrian safety performance evaluation model is built. The model can correctly detect nearly 94.4% of possibly dangerous traffic scenes. Other kinds of mixed traffic scenes can also be studied based on this research.