Most of the past research on platooning control has not considered the impact of changes in the level of trust between vehicles in the platoon on the platoon control, and the communication process between connected vehicles is often subjected to malicious attacks such as time delays or interruptions, as well as tampering with status information, and the level of trust between vehicles changes, which affects the cooperative behaviors such as driving styles and inter-vehicle interval strategies. Changes in the level of trust between vehicles can affect the control process strategy of the vehicle, such as where the spacing strategy changes, thereby affecting the inputs to the controller, which in turn affects the outputs of the controller, and ultimately affects changes in the driving style of the vehicle, so considering the level of trust between vehicles in vehicle platoon control is critical to the safe operation of the vehicle platoon. To address this challenge, this paper proposes a dynamic splitting and merging control strategy for vehicle platoons based on the trust evaluation of vehicle nodes. Firstly, trust is evaluated using the Certainty Factor (C-F) uncertain reasoning model, which is a process that starts from initial evidence of uncertainty and derives reasonable conclusions with a certain degree of uncertainty by utilizing the uncertainty of evidence. An autoregressive model predicts short-term vehicle trajectories, and multi-source information from communication and perception is compared to determine vehicle node trust. Utilizing Bayesian reasoning methods, the trust level of vehicle nodes is updated. Based on traditional platoon control and trust evaluation, a software-level dynamic splitting and merging strategy is proposed to enhance resilience against unknown disturbances in a zero-trust environment. Finally, the system’s internal and string stability are analyzed, and the scheme’s effectiveness is validated through simulations. Note to Practitioners—The motivation of this paper is to solve the problem of vehicle platoon security control for connected vehicles in default distrust scenarios. Previous approaches were designed under the premise of considering mutual trust between vehicle nodes, ignoring the problems of inaccurate information interaction between connected vehicles and attacked interaction processes in real scenarios. To resist the risks of various types of attacks faced by intelligent networked vehicles in real traffic scenarios, this paper designs a vehicle platoon control strategy under zero-trust scenarios. Firstly, using the C-F uncertainty reasoning method, we propose a data-based node trust evaluation algorithm, and utilize its node trust evaluation results to re-establish the vehicle state equation under the zero-trust scenario. To make the vehicle platoon control under the zero-trust scenario scalable and resilient, this paper further proposes a vehicle platoon dynamic splitting and merging strategy based on the vehicle node trust evaluation scheme without changing the original communication topology of the vehicle platoon. The preliminary experimental results show that compared with the previous vehicle platoon control method under default vehicle node trust, the security of vehicle platoon operation is effectively improved. In the future, to better match real traffic scenarios, we will study how to dynamically adjust the trust threshold of vehicle nodes based on the vehicle operation environment, spacing strategy, sensor parameters, and other factors.

Extended object detection and tracking (EODT) is becoming a promising alternative for autonomous perception, which provides not only common motion states but also accurate spatial extent information, such as shape and size estimations. However, due to uncoordinated radar transmissions in zero-trust autonomous driving scenarios, radar-based EODT systems suffer from mutual radio frequency (RF) interference launched by attackers, leading to ghost targets and increased noise. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper. In contrast to pioneering works that treat object detection and tracking as two separate steps, the proposed method handles them jointly by integrating a continuous detection process into tracking, improving the detectability of weak targets. More specifically, to accommodate the time-varying number and extended size of radar reflections, an adaptive spatial distribution model representing the deformable extents is incorporated to capture the contour evolution over time. The key insight is that by accumulating the reflected power, all backscattered points are regarded as one entity to match the real target so that the intractable data association problem can be circumvented in the proposed method. Unlike the prominent random matrix model-based approaches that split motion and extent states into independent parts, this study explores the interdependencies between the states and updates them simultaneously. In addition, the proposed system has been deployed on a low-cost automotive radar platform. Experimental results confirm that the proposed approach can achieve accurate and resilient EODT against RF interference attacks, especially in occlusion, dynamic motion switching, and complex multiple extended target tracking scenarios. A demonstration video with EODT results is available in the supplementary materials.

Due to the small radar cross section (RCS), pedestrians are typical low-observable traffic participants for radar-based automotive perception systems. The early detection and understanding of pedestrians’ activities are of great significance to automotive safety. To this end, this paper presents an end-to-end joint tracking and activity estimation (RETA) system based on 4D automotive radar, which deals in particular with pedestrian activity identification under cluttered real-world scenes. Firstly, a novel integrated detection and tracking algorithm is proposed to guarantee positioning accuracy, in which all unthresholded 4D radar measurements are incorporated to explore the spatial coherent information across multiple frames, avoiding weak target information loss. After that, to discriminate continuous activities with varying durations in sequential trajectories, this paper innovatively presents a decomposed connectionist recurrent convolutional neural network, which facilitates fused temporal-spatial motion feature extraction. Especially, the labor-consuming activity pre-segmentation problem is circumvented with the help of a connectionist temporal classification algorithm in the proposed neural network. At last, RETA can be implemented for real end-to-end perception applications. Extensive experiment results highlight its superiority and effectiveness by attaining a continuous recognition accuracy of 94.8%. To the best of our knowledge, this is the first end-to-end activity recognition system specific for low-observable pedestrians. A demonstration video recorded in challenging practical traffic scenarios has been uploaded in the supplementary materials.

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 article presents a spatial–temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a 4-D 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 4-D 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 0.3851 m in low SNR conditions.

Due to the weak radar echoes and strong background clutters in low-altitude airspace, the detection and tracking for multiple micro-unmanned aerial vehicles (UAVs) have posed formidable challenges in radar surveillance field. Consequently, this paper proposes an end-to-end detection and tracking framework (E2DTF) for multiple micro-UAVs by utilizing the frequency modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar. To address the low signal-to-noise ratio (SNR) problem, E2DTF presents a frame-range-Doppler-azimuth information fusion filter to integrate the target energy by exploiting the spatio-temporal dependence of positions within a sequence of unthresholded frames. Additionally, considering that a target may enter/leave the radar field-of-view (FOV), E2DTF introduces a target model state, updated by an extended Markov state transition matrix sequentially, to realize an unknown, time-varying number of micro-UAVs tracking. Another nice feature of E2DTF is that it avoids the complex data association procedure thanks to removing the threshold-decision operation. Finally, both numerical simulations and experiments with five different scenarios, i.e., horizontal line, cross-trajectory, circular loop, rainy condition and 3D trajectory tracking are presented to verify the effectiveness of the proposed method. The results show that E2DTF can obtain superior detection and tracking performance for multiple micro-UAVs in contrast to the state-of-the-art methods considering detection and tracking processes independently, especially under low SNR conditions.

Robust pedestrian trajectory-tracking is an essential prerequisite to traffic accident prevention. However, it is a challenging task in urban autonomous driving, since the weak backscattered signals from pedestrians with small radar cross-section may be submerged in strong background clutters, especially under adverse weather conditions. On this account, this article presents an integration of detection and tracking (iDT) toward multipedestrian with a low signal-to-noise ratio (SNR). In particular, in contrast to conventional methods, in which the detection and tracking are treated as two separate processes, we address them jointly to ensure the accuracy of continuous detection and tracking in low SNR conditions. Another distinguishing element is that to accommodate the time-varying number of targets, the Bayesian framework is tailored by augmenting the state vector with a multipedestrian evolutional indicator. The advantage is that all targets can be tracked simultaneously by searching the global likelihood ratio of a spectrum once, rather than assigning an individual tracker to each target in conventional methods. Furthermore, through the proposed integrated framework, the data association problem is circumvented because there is no explicit measurement-target assignment process in our approach. In addition, a commercial automotive multiple-input-multiple-output millimeter-wave radar sensor is employed to validate the proposed method. Consequently, numerous simulation and experiment results turn out that iDT shows unique advantages in low-observable multipedestrian tracking compared with traditional methods.

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.