The rapid development of autonomous driving technology has driven continuous innovation in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key sensing devices. Leveraging its all-weather operational characteristics and robust perception capabilities in challenging environments, 4D mmWave radar plays a crucial role in achieving highly automated driving. This review systematically summarizes the latest advancements and key applications of 4D mmWave radar in the field of autonomous driving. To begin with, we introduce the fundamental principles and technical features of 4D mmWave radar, delving into its comprehensive perception capabilities across distance, speed, angle, and time dimensions. Subsequently, we provide a detailed analysis of the performance advantages of 4D mmWave radar compared to other sensors in complex environments. We then discuss the latest developments in target detection and tracking using 4D mmWave radar, along with existing datasets in this domain. Finally, we explore the current technological challenges and future directions. This review offers researchers and engineers a comprehensive understanding of the cutting-edge technology and future development directions of 4D mmWave radar in the context of autonomous driving perception. 4D mmWave radar, autonomous driving, perception, multi-modal fusion, 3D object detection.

Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.

During our discussion at workshops for writing “What Does ChatGPT Say: The DAO from Algorithmic Intelligence to Linguistic Intelligence” [1], we had expected the next milestone for Artificial Intelligence (AI) would be in the direction of Imaginative Intelligence (II), i.e., something similar to automatic words-to-videos generation or intelligent digital movies/theater technology that could be used for conducting new “Artificiofactual Experiments” [2] to replace conventional “Counterfactual Experiments” in scientific research and technical development for both natural and social studies [2]–[6]. Now we have OpenAI's Sora, so soon, but this is not the final, actually far away, and it is just the beginning.

This perspective paper delves into the concept of foundation intelligence that shapes the future of smart infrastructure services as the transportation sector transitions into the era of Transportation 5.0. First, the discussion focuses on a suite of emerging technologies essential for foundation intelligence. These technologies encompass digital twinning, parallel intelligence, large vision-language models, traffic simulation and transportation systems modeling, vehicle-to-everything (V2X) connectivity, and decentralized/distributed systems. Next, the paper introduces the present landscape of Transportation 5.0 applications as illuminated by the foundational intelligence, and casts a vision towards the future including cooperative driving automation, smart intersection/infrastructure, parallel traffic management, virtual drivers, and mobility systems planning and operations, laying out prospects that are poised to redefine the mobility ecosystem. Last, through a comprehensive outlook, this paper aspires to offer a guiding framework for the intelligent evolution in data generation and model calibration, digital twinning and simulation, scenario development and experimentation, feedback loop for management and control, and continuous learning and adaptation, fostering safety, efficiency, reliability, and sustainability in the future smart transportation infrastructure.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

Remote sensing semantic segmentation plays a significant role in various applications such as environmental monitoring, land use planning, and disaster response. CNNs have been dominating remote sensing semantic segmentation. However, due to the limitations of convolution operations, CNNs cannot effectively model global context. The success of Transformers in the NLP domain provides a new solution for global context modeling. Inspired by Swin Transformer, we propose a novel remote sensing semantic segmentation model called CSTUNet. This model employs a dual-encoder structure consisting of a CNN-based main encoder and a Swin Transformer-based auxiliary encoder. We first utilize a detail-structure preservation module (DPM) to mitigate the loss of detail and structure information caused by Swin Transformer downsampling. Then we introduce a spatial feature enhancement module (SFE) to collect contextual information from different spatial dimensions. Finally, we construct a position-aware attention fusion module (PAFM) to fuse contextual and local information. Our proposed model obtained 70.75% MIoU on the ISPRS-Vaihingen dataset and 77.27% MIoU on the ISPRS-Potsdam dataset.