The main form of freeway traffic congestion is the familiar stop-and-go wave, characterized by wide moving jams that propagate indefinitely upstream provided enough traffic demand. They cause severe, long-lasting adverse effects, such as reduced traffic efficiency, increased driving risks, and higher vehicle emissions. This underscores the crucial importance of artificial intervention in the propagation of stop-and-go waves. Over the past two decades, two prominent strategies for stop-and-go wave suppression have emerged: variable speed limit (VSL) and jam-absorption driving (JAD). Although they share similar research motivations, objectives, and theoretical foundations, the development of these strategies has remained relatively disconnected. To synthesize fragmented advances and drive the field forward, this paper first provides a comprehensive review of the achievements in the stop-and-go wave suppression-oriented VSL and JAD, respectively. It then focuses on bridging the two areas and identifying research opportunities from the following perspectives: fundamental diagrams, secondary waves, generalizability, traffic state estimation and prediction, robustness to randomness, simulation scenarios for strategy validation, and field tests and practical deployment. We expect that through this review, one area can effectively address its limitations by identifying and leveraging the strengths of the other, thus promoting the overall research goal of freeway stop-and-go wave suppression.

In the 6G-enabled intelligent transportation systems (ITS), each intelligent transportation terminal needs to perform long-distance, low-latency image interaction to ensure real-time information exchange, including real-time vehicular environmental images and various vehicular media images. However, due to high computational cost and large computing resource usage, many learning-driven image compression models are difficult to deploy on intelligent transportation terminals such as edge devices and connected vehicle terminals in the 6G-enabled ITS to reduce the transmission resource consumption of massive image data from ITS. To address the above problems, this paper proposes a high-throughput lightweight Transformer model for image compression tasks on ITS-based intelligent transportation terminals. By constructing a lightweight Transformer combination and reducing the use of Transformer blocks by internally connecting convolutional blocks, the computational cost of the model is reduced. Furthermore, this paper proposes a lightweight Swin Transformer module, which further reduces the resources required for model calculation by directly connecting window multi-head self-attention (W-MSA) and shifted window multi-head self-attention (SW-MSA). In addition, this paper designs a simplified entropy model, which speeds up the execution time of the entropy model and improves the throughput of the overall network model by directly fusing the latent representation of the images. Experimental results show that our proposed model has significantly improved throughput and execution time compared with the state-of-the-art models on two ITS public datasets.

This paper introduces a diffusion process-based approach to address trajectory generation and vehicle propagation within traffic networks. This approach presents dynamic transition probabilities to dictate the navigation of trips through the network. The proposed probabilities incorporate connectiveness and attractiveness of network cells with propagation threshold associated with trip length to effectively replicate real-world observations. This enables trajectory generation that closely adheres to real-world land-use criteria and traffic patterns. The generation of trajectories within the network arises from a purely data-driven methodology, which bypasses the direct use of the city’s network file. Instead, it uses the demand pattern to reconstruct the network through the application of granular Voronoi cells. This approach offers a more refined spatial representation resulting in cells of different sizes as a function of the demand level. Trajectories were generated by fusing datasets of real-world trips, land-use, and network characteristics from Daejeon, South Korea, ensuring a robust calibration and validation process. Compared to a Recurrent Neural Network (RNN) model and a Mobility Markov Chain (MMC) model, our model is superior in terms of cell and link visits counts, complexity similarity with original dataset, correct cell sequence prediction, and Origin-Destination flow pattern that follows the pattern of original trips. The findings underscore the potential of diffusion process-based approaches to enhance transportation planning, showing that non-Artificial Intelligence (non-AI), data-driven methodologies can perform comparably to AI-driven techniques. These methods generate trajectories that closely reflect real-world traffic dynamics while aligning with land-use criteria, offering an explainable, transparent and robust alternative.

Autonomous driving technology has garnered significant attention for its potential to reduce driver burden and enhance road safety. Modern autonomous driving systems rely on a variety of sensors to perceive complex driving environments. Many existing methods map heterogeneous data into the bird’s eye view (BEV) space for feature fusion. However, they often fail to fully exploit the cross-modal interactions between cameras and LiDAR, or incorporate temporal information, resulting in suboptimal performance. Furthermore, commonly used fusion strategies are often overly simplistic. This study proposes BEV-CMHF, a cross-modality hybrid fusion framework for BEV 3D object detection with feature interaction and temporal fusion. By introducing an interactive cross-attention module and a long-short-term temporal module, the proposed framework enhances the representational power of fused BEV features. Specifically, a feature-interaction attention module that facilitates effective interaction between the camera and LiDAR BEV features using deformable attention is designed, providing guidance and supervision for the camera BEV features. Subsequently, a historical feature temporal fusion module that integrates the long-short-term temporal module is introduced to incorporate additional critical temporal information into the BEV features. Moreover, a dynamic hybrid feature-fusion module is designed to fuse the BEV features of the camera and LiDAR effectively through a hybrid attention mechanism that combines coarse and fine attention. Extensive experiments conducted on the nuScenes benchmark validate the effectiveness of the proposed method, achieving 70.87% mAP and 74.00% NDS on the test set. Using a single NVIDIA GeForce RTX 4090, the method attained an inference speed of 5.79 images per second (5.79 img/s), corresponding to an inference time of 172.64 ms on the nuScenes dataset. The source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BJUTsipl/BEV-CMHF</uri>