Cloud-based intelligent connected vehicle (CICV) provide new approaches to realising autonomous driving, and the relatively open wireless communication network of the vehicle cloud makes it vulnerable to cyber-attacks. The cyber-attacks that penetrate the vehicle system tamper with or interrupt the existing control signals, potentially leaving the vehicle system’s actuators in an unsafe operating region for an extended period of time, thereby increasing the risk of actuator fault. For avoiding the degradation of path tracking accuracy and driving stability of CICVs under the co-existence of denial-of-service (DoS) attacks and steering system actuator motor fault, this paper proposes a robust security control method based on time-lag state observation under the dynamic event triggering (DET) at the network layer. Firstly, a closed-loop control system including non-uniform triggering period delay, DoS attack delay, steering system fault and external perturbation under DET policy of the vehicle cloud wireless communication network is established. Secondly, the sideslip angle before the delay is estimated based on the reduced-order Kalman filtering method and a robust observer is further constructed to observe the system state and steering system faults after the delay. Thirdly, an observer-based dynamic output feedback robust safety controller is designed with the path tracking accuracy and stability of the vehicle as the control objectives. Then, an electromechanical braking (EMB) clamping force distribution controller was proposed to execute the additional yawing moment calculated by the above controller. Finally, simulations and HiL tests were performed under typical operating conditions for validation. The results indicate that the proposed DET scheme reduces the communication load by 23.5% compared with the time-triggered and static strategies while maintaining comparable control performance. Under DoS attacks, the proposed safety controller decreases the average lateral displacement error and heading angle error by 171.2% and 158.7%, respectively, relative to the conventional model predictive control (MPC).

The development of intelligent connected vehicles and cloud-based control technologies offers great potential for high-level autonomous driving. In practical applications, the vehicle to infrastructure (V2I) channel between the vehicle and roadside edge cloud suffers from channel fading and quantization errors, which substantially affect lateral control performance. To address this problem, this paper develops a control framework that explicitly models and compensates for V2I uncertainty. Firstly, a V2I channel simulation model is constructed to analyse the quantization error and obtain the quantitative error ratio variance (QERV). Meanwhile, the V2I channel parameters of vehicles in multi-scene and multi-condition operation are collected to train a channel fading error ratio variance (CFERV) prediction model based on bidirectional long short term memory (Bi-LSTM) network. Secondly, a control-oriented V2I channel uncertainty model is developed to capture channel fading and quantisation effects, based on which a cloud-based intelligent connected vehicle (CICV) lateral control model with channel uncertainty is established. Then, the CICV lateral model is reformulated as a linear stochastic system, upon which a mixedH2/H∞controller with integrated probabilistic safety constraints is synthesised to ensure multi-objective performance and lateral safety. Finally, simulation and semi-physical in the loop experiments are performed under typical conditions, and the results show that the proposed control strategy effectively improves the lateral control accuracy of the vehicle in cloud control scenarios and ensures the ride comfort of the vehicle.

In the field of automated driving, scenario-based safety testing is essential for ensuring vehicle safety and promoting technological development. However, traditional mileage-based real-world testing methods are limited by the massive testing requirements and extremely long cycles, hardly meeting efficient validation demands. To address this, this paper proposes a Hybrid Optimal Acceleration Evaluation (HOAE) model based on virtual testing. First, a hybrid segmentation model based on fitting error is constructed to accurately characterize the distribution features of naturalistic driving data, and an acceleration model is designed with the number of tests as the optimization objective to solve for the optimal number of segments. Second, the Importance Sampling (IS) method is introduced to increase the occurrence probability of risk scenarios, and the optimal IS function is solved based on the above acceleration model. Finally, a simulation test platform is established, and comprehensive comparative experiments are conducted under various acceleration evaluation methods, indicators, and parameter distributions. The results demonstrate that the proposed HOAE method offers better universality and higher testing efficiency, and it can increase the test mileage to 104times of the Monte Carlo (MC) method, thereby advancing safety testing of automated vehicles toward greater efficiency and broader applicability.

Skill learning from language instructions is a critical challenge in developing intelligent agents that can generalize across diverse tasks and follow complex human instructions. Hierarchical methods address this by decomposing the learning problem into multiple levels, where the high-level and low-level policies are mediated through a latent plan space. Effective modeling and learning of this latent plan space are key to enabling robust and interpretable skill learning. In this paper, we introduce LADS, a hierarchical approach that learns language-conditioned discrete latent plans through semantic skill abstractions. Our method decouples the learning of the latent plan space from the language-conditioned high-level policy to improve training stability. First, we incorporate a trajectory encoder to learn a discrete latent space with the low-level policy, regularized by language instructions. Next, we model the high-level policy as a categorical distribution over these discrete latent plans to capture the multi-modality of the dataset. Through experiments in simulated control environments, we demonstrate that LADS outperforms state-of-the-art methods in both skill learning and compositional generalization.

Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world.

Cloud-based intelligent connected vehicles (CICVs) are among the essential future applications of autonomous driving. Communication between the vehicle and the cloud is carried out through a wireless network, which is susceptible to cyber attacks due to its openness. In order to solve the problem of denial-of-service (DoS) attacks that result in large trajectory tracking errors for CICV, a cross-layer collaborative resilient control strategy is proposed, which integrates offensive-defensive games at the network layer withH∞ control at the physical layer. Firstly, based on the vehicle dynamics model and the DoS attack model, the closed-loop vehicle trajectory tracking control model under DoS attack in sensor-controller (S-C) and controller-actuator (C-A) communication networks is established. Secondly, an offensive-defensive gaming framework in network layer is established, and a cost function considering vehicle control error, attack frequency and attack duration of DoS is designed to optimise the hybrid gaming strategy, thereby constraining the packet loss rate (PLR) caused by DoS attacks. Then, a controller design framework integratingH∞ control and multi-objective co-optimisation is proposed to maintain the comprehensive control performance of vehicle system under DoS attack. Finally, the results of simulation and cloud controller-in-the-loop experiment under typical conditions show that the proposed offensive-defensive gaming strategy in network layer can effectively suppress DoS attacks, and the designed controller improves trajectory tracking accuracy by 47.5% and 147.6% compared to the conventional model predictive controller (MPC) and linear quadratic regulator (LQR), respectively, which significantly enhances the cyber-physical security of the CICV.

Planning paths for Frenet-based autonomous vehicles (AVs) at roundabouts is difficult without complete and smooth reference paths. In such situations, the interpolating curve planner is often used to create segmented reference paths from simplified geometric roundabout data. While this method ensures curvature continuity within each curve segment, the continuity at the junctions of these segments is poor. Additionally, the determination of merging and diverging point positions at roundabouts has not been thoroughly explored. This paper introduces a novel approach using 5th-order Bézier curves to plan piecewise reference paths for AVs at roundabouts. The proposed method enhances endpoint curvature continuity of the Bézier curves and improves adaptability to non-standard roundabouts. A well-designed objective function is created to optimize both the geometric continuity parameters of the Bézier curves and the positions of merging and diverging points in the circulatory roadway. This function takes into account key factors, including path length and smoothness. Case studies validate the feasibility of maintaining curvature continuity at the endpoints and the method’s ability to generalize across various scenarios, proving its effectiveness for different roundabout structures. The results also confirm the method’s efficacy in generating paths from original geometric roundabout data. Lastly, the acceptable transverse deviations between real-world trajectories and reference paths demonstrate the rationality and practical applicability of this method.

Exploration in decentralized cooperative multi-agent reinforcement learning faces two challenges. One is that the novelty of global states is unavailable, while the novelty of local observations is biased. The other is how agents can explore in a coordinated way. To address these challenges, we propose MACE, a simple yet effective multi-agent coordinated exploration method. By communicating only local novelty, agents can take into account other agents' local novelty to approximate the global novelty. Further, we newly introduce weighted mutual information to measure the influence of one agent's action on other agents' accumulated novelty. We convert it as an intrinsic reward in hindsight to encourage agents to exert more influence on other agents' exploration and boost coordinated exploration. Empirically, we show that MACE achieves superior performance in three multi-agent environments with sparse rewards.

The lane-changing behavior exerts a profound influence on the dynamic attributes of traffic flow and road safety. Accurate analysis of lane-changing behavior not only facilitates the comprehension of traffic phenomena and the prevention of traffic accidents but also contributes to constructing a dynamic and realistic background traffic flow for autonomous driving tests. In this paper, we propose a two-dimensional lane-changing dynamics model based on force by abstracting the motion of the vehicle as the variation in acceleration under the influence of forces. Specifically, based on the similarity analysis of factors affecting acceleration and constitutive relationships, the three types of forces to which the vehicle is subjected are represented by a combination of basic visco-elastic elements, while the driver’s attention to the current and target lanes decreases and grows during the lane changing process is analyzed. This model not only comprehensively delineates the lane-changing process but also represents the influences of driver characteristics, neighboring vehicles, and road conditions. In order to verify the performance of the proposed model, we identified the parameters using the least squares method (LSM) based on the highD naturalistic driving dataset. Comparative results illustrate that the lane-changing dynamics model effectively capture complex lane-changing behaviors and their interactions between ego vehicle and surrounding vehicles through a simple and unified way.

When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before. Modeling the behaviors, goals, or beliefs of opponents could help the agent adjust its policy to adapt to different opponents. In addition, it is also important to consider opponents who are learning simultaneously or capable of reasoning. However, existing work usually tackles only one of the aforementioned types of opponents. In this paper, we propose model-based opponent modeling (MBOM), which employs the environment model to adapt to all kinds of opponents. MBOM simulates the recursive reasoning process in the environment model and imagines a set of improving opponent policies. To effectively and accurately represent the opponent policy, MBOM further mixes the imagined opponent policies according to the similarity with the real behaviors of opponents. Empirically, we show that MBOM achieves more effective adaptation than existing methods in a variety of tasks, respectively with different types of opponents, i.e., fixed policy, naive learner, and reasoning learner.

The existing trajectory prediction is mainly for specific road sections, which is poor to adapt to complex and changing traffic scenarios. Meanwhile, decentralized trajectory data is hard to be fully utilized in the data silo environment. To solve data silos in the intelligent vehicle industry, introducing federated learning methods in vehicular edge computing has attracted extensive attention. But traditional federated learning still has the potential to suffer from the mining of training data in the case of model privacy leakage. In this paper, a vehicle trajectory prediction method based on federated learning and homomorphic encryption has been presented, which adopts a three-layer architecture with a vehicle cluster, edge computing server, and cloud core network. Compared with traditional centralized deep learning methods, this approach enables joint modeling of multiple parties to improve the model's generalization performance. We used a proxy re-encryption algorithm to implement key distribution and also designed an encrypted federated network algorithm FAHEFL, which uses FAHE1 homomorphic encryption to protect the privacy of the model in parameter transmission. Each local model includes a driving behavior recognition module and trajectory output module. The driving behavior recognition module uses a 1D convolutional neural network to recognize the driving behavior, then input recognition results and historical trajectories to the trajectory output module, which uses LSTM neural network to output predicted trajectories. The experiment results show that the model built with FAHEFL has no more 1% error in the driving behavior recognition module than concentrated learning, while the minimum mean square error of the trajectory prediction module increased by only 2.5%. This article also discusses the performance between FAHEFL and well-known cryptographic federation network algorithms.

The ultimate goal of self-driving technologies is to offer a safe and human-like driving experience. As one of the most important enabling functionalities, trajectory planning has been extensively studied from the perspective of safety. However, human-like trajectory planning on curved roads has rarely been studied. In this paper, we characterize and model human driving using extensive experimental driving collected on an urban curved road with 30 participants (10 experienced and 20 novice drivers) and five vehicles of different types. Differential global positioning system (GPS) is used to measure vehicle positions in high precision. We study factors that affect the driving trajectory, including vehicle speed, road curvature, and sight distance. We find that the human drivers typically do not follow lane centerline and the human-driven trajectories are very different from planners like rapidly exploring random tree (RRT). To generate human-like driving trajectory, we develop a data-driven trajectory model using general regression neural network (GRNN). The model was validated in various cases with promising performance.