The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross-section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this paper presents a spatial-temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a four-dimensional (4D) multiple-input-multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851m in low SNR conditions.

The communication topologies between vehicles in a platoon substantially impact the platoon’s stability. This study provides a distributed linear feedback control law that considers time-varying delay with guaranteed internal and string stability of the platoon system under various communication topologies. Firstly, the vehicle dynamics linearized model was derived using the precise feedback linearization technology. Different types of communication topologies, such as vehicle-to-vehicle communication and sensor-based communication, were described using directed graphs. Secondly, the linear feedback control law was designed to establish the stable zone of the linear controller gain under the effect of different communication topologies using directed graphs and the Routh-Hurwitz stability theorem. The Lyapunov-Razumikhin theorem determines the upper bound of the time-varying delay of various communication topologies. Additionally, the string stability of leader-predecessor following topology was studied, and the results were combined with the internal stability to determine the upper bound of time-varying delay. Finally, the results were verified by conducting two numerical simulations.

Directed against the shortcoming of the vulnerability assessment based on complex network theory for urban road network traffic systems, a state vulnerability assessment method, considering the influence of congestion, is constructed by using a cloud model to describe the randomness and uncertainty characteristics of risks. First, based on complex network theory, the primary index assessment system of road network vulnerability is introduced. Second, to describe the congestion states of roads, the cloud model theory is introduced to characterize the congestion features of road sections. After that, a state vulnerability identification method based on congestion cloud charts is constructed. Finally, on the basis of topological mapping of road network in Nan'an District in Chongqing, experiments and analyses are carried out in light of the actual congestion delay index dataset provided by AutoNavi Maps to verify the effectiveness and rationality of our proposed scheme. The experimental results show that the assessment method of state vulnerability can better describe the overall operational state and vulnerable road sections for road network.

With the rapid development of vehicular networking and intelligence, more interfaces are adopted by cars to interact with the external world. Accordingly, this also brings enormous security risks, which are potentially catastrophic due to communication loopholes. Since the Controller Area Network (CAN) is critical to the transmission of commands among vehicular components, it has become a prime target for hacker research and attack. Considering that the CAN bus is commonly used and its protocol is always flawed, how to efficiently detect the intrusions against it has become an evitable problem. In this paper, we presented an intrusion detection system that can be rapidly deployed inside the vehicle. Aiming at achieving the goal of real-time detection, we devised a feature extraction algorithm with low complexity and thoroughly exploited its advantages via a GRU-based lightweight neural network. The experiment was physically conducted on in-vehicle embedded devices using publicly available datasets. Experiment results illustrated that our intrusion detection system could be rapidly deployed with high classification and real-time performance. Moreover, we also discussed how an intrusion detection system could work with OTA services to improve the intelligence of vehicular operating systems and prevent potential attacks.

The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and malicious attacks between vehicles. Motivated by these observations, we have investigated the zero-trust theory and framework based on the authorization and confirm processes among connected nodes in a group. Furthermore, we have analyzed and summarized the information perception, full information chain communication, and fault-tolerant control mechanism of ICV systems under a zero-trust framework, in addition to the methods of trust value calculation and heterogeneous vehicle platoons. The results indicated that zero-trust theory has a strong potential in group reliability research.