An accurate perception of traffic environments is essential for safe autonomous driving. LiDAR point cloud semantic segmentation, an essential capability, is a key technology for accurately perceiving traffic elements. Feature extractions for LiDAR semantic segmentation present difficulties due to several factors. To cite a typical example, the vast-scale variations in objects in complex traffic scenes make it difficult to simultaneously capture the fine-grained details of small objects and the contextual information of large objects. To address these types of issues, this paper proposes a Multiscale Agent-gated Sensor Fusion (MASF) method, which projects 3D point clouds into 2D feature maps, multiscale feature extraction, and adaptive cross-modal fusion mechanisms. First, a 3D point cloud is projected into 2D feature maps, and its features are extracted alongside camera images in a dual-stream encoder. At each encoder stage, the camera features are fused into the LiDAR stream. To refine the fused multimodal features, this paper develops a Multiscale Gated Bottleneck Convolution (MS-GBC) mechanism to adaptively select features across different spatial scales. Second, at the encoder’s bottleneck, the fusion process is handled by the Low-Rank Agent Attention (LRAA) mechanism, which introduces a compact set of agent tokens to capture global dependencies across modalities. Third, a decoder progressively upsamples the context-rich feature map from the encoder, reintegrating fine-grained details through skip connections. Then, a segmentation head generates the final segmentation results. Finally, experiments on two large-scale real-world datasets show that the performance of MASF is superior to that of many baseline methods. For example, it achieves mean intersection-over-union (mIoU) improvements of 2.6% and 2.0% over the best LiDAR-only methods on the two datasets.

The rapid development of the Internet of Vehicles (IoV) has accelerated technological progress, but several critical security challenges remain, especially in the context of vehicle trust management. Two representative issues are malicious nodes and unreliable information transmission. To address these problems, we propose BiTrustChain, a dual-layer blockchain framework designed to enhance security and trust management in IoV environments. First, it consists of two innovative data chains: a Behavior Data Chain (BDC) and a Reputation Evaluation Chain (REC). The BDC records vehicle interaction data, whereas the REC stores and updates the trust values in real time. Second, within this framework, we develop a Multifactor Bayesian Reputation (MFBR) model that enables quantitative evaluation of node trustworthiness. It integrates a time-decay function and a penalty mechanism to regulate reputation evolution. The trust values decrease after malicious behaviors and recover through continuous normal interactions. In addition, we propose a dynamic local whitelist for indirect reputation evaluation. It filters out untrustworthy nodes and ensures that only reliable nodes remain. The filtered indirect trust is then combined with direct trust to produce a comprehensive reputation score. Third, we design a new set of event-driven smart contracts to synchronize the BDC and REC in real time and ensure secure and efficient data exchange. Finally, we performed experiments on the evaluation platform SUMO/NS-3, and the results show that our method identifies malicious nodes with higher accuracy. In particular, the framework achieves 1.5× higher throughput and reduces latency by 40% compared to the baseline single-chain system. The framework also enhances interaction data integrity and improves robustness against adversarial reputation manipulation.

3D object detection plays a pivotal role in facilitating comprehensive scene understanding in autonomous driving systems. One of its key challenges is to achieve accurate perception in complex environments. Compared with LiDAR systems and stereo-vision approaches, monocular camera-based solutions are more cost-effective and easier to deploy. However, the absence of depth in monocular images hinders the accurate localization of 3D bounding boxes when only monocular images are used. This work proposes MonoLS, a monocular 3D object detection framework that incorporates lightweight multi-scale feature fusion and spatially-aware attention. It aims to address the challenge of missing depth information while achieving precise object localization. First, lightweight multi-scale feature fusion combines deep and shallow features. This design allows for effective multi-scale feature extraction without compromising real-time detection capabilities. Second, spatially-aware attention employs a dual-branch structure, with the spatial branch using a triplet attention to capture spatial details, and the context branch aggregating global context information through global attention. These two branches are subsequently fused to produce enhanced feature representations that preserve spatial distribution and semantic richness. Finally, experiments on the KITTI dataset demonstrate that our method outperforms the baseline, achieving a real-time inference speed of up to 67 FPS.

As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.

In intelligent transportation systems, data routing in vehicle-to-everything (V2X) networks is key to ensuring efficient information transfer among vehicles, pedestrians, and infrastructure. The quality of data routing directly affects communication efficiency and system performance. However, data routing in V2X networks often faces potential security threats, which may lead to communication interruption, data delay, or information loss. Unreliable routing fails to meet the communication Quality of Service (QoS) requirements for V2X networks. Therefore, this article proposes a joint scheme that combines trust prediction and attack resistance to ensure reliable routing in V2X networks. First, this scheme employs a fuzzy control-based trust evaluation method to provide direct trust indicators. Second, a trust prediction method based on deep belief networks is utilized to evaluate vehicle status. A classification scheme based on the trust levels is used to filter candidate sets for network repair to help the network resist malicious behavior. Finally, a novel routing decision function is introduced to plan reliable routes. Routes planned on the basis of this function not only meet the basic requirements of reliable routing but are also suitable for routing requirements in different scenarios, such as minimizing transmission latency. The experimental results show that, compared with the three baseline schemes, this scheme improves the accuracy and false alarm rate on the UNSW-NB15 dataset by 2.94% and 6.31%, respectively, and this scheme also performs better in terms of the data reception rate and transmission delay rate in actual application scenarios.

The increasing need for communication capabilities in mobile devices has led to the recognition of mobile edge computing (MEC) as a critical solution for addressing computationally intensive and latency-sensitive tasks due to its widespread distribution of resources close to devices. However, in scenarios such as disaster response and emergency rescue, the rapid deployment of edge servers to handle tasks may be challenging. Therefore, unmanned aerial vehicle (UAV)-assisted MEC systems have garnered significant interest due to their ease of deployment and high mobility. Nonetheless, the limited computational resources and sensing radius of UAVs give rise to the challenge of optimizing target area coverage and mission data processing timeliness within a restricted time period. In response to this challenge, we present PCRDAC, a novel reinforcement learning-based mobility management framework for UAVs. This framework periodically instructs UAVs to collaboratively update their decision networks, thus determining their movement patterns. This framework can also control UAVs to support worst-case scenarios. Comprehensive simulation experiments validate the efficacy of our framework. Our framework promotes efficient collaboration among UAVs and significantly reduces data staleness in the system. As a result, edge devices can collect ambient data that is fresh enough.