Motion control of autonomous electric vehicles (AEVs) faces severe challenges due to significant uncertainties introduced by dynamic environments, which may lead to potential safety issues. To address this problem, this paper proposes a meta-learning-enhanced online adaptive control method for AEVs to realize robust and high-precision motion control. First, a novel Meta-Learning-based Online Adaptive (MLOA) modeling approach is introduced, which enables rapid online adaptation of vehicle dynamics through few-shot learning combined with real-time operational data. This approach effectively captures dynamic behaviors in previously unseen tasks. Furthermore, the MLOA model is integrated into a Stochastic Model Predictive Control to enhance control adaptability and responsiveness under various conditions. Meanwhile, chance constraints are incorporated to handle random disturbances, thereby strengthening the robustness of the control strategy. The proposed method is validated through both simulations and real-vehicle experiments. Results show that the controller adapts within 1.8 s in previously unseen tasks and achieves up to 72.6% reduction in lateral tracking errors compared to the baseline method, and maintains an average computation time of only 0.0148 s per control step. These findings confirm the proposed method’s ability to maintain high trajectory tracking accuracy, fast response, and real-time feasibility under uncertainties and highlight the effectiveness of combining meta-learning with optimal control, providing a robust and adaptive control framework for autonomous driving in diverse and complex environments.

Vehicle electrification presents challenges and opportunities across multiple sectors, including the automotive, energy and infrastructure domains. Battery charging and swapping are the two primary technologies for refuelling electric vehicles (EVs). However, the involvement of multiple participants and various factors makes EV refuelling a complex and multi-domain issue. Since conventional conductive charging requires vehicles to remain stationary for a period of time, parking naturally provides opportunities for EV charging. Therefore, parking and EV charging are intrinsically connected in how they are organised and planned. This paper presents a comprehensive literature review on the features of EV refuelling demand and its relation to parking patterns. The review focuses on key study issues related to the interaction between EVs and the power grid, namely forecasting, planning, and scheduling. These issues are examined at three different scales: the individual, station, and regional levels. Based on the findings from the literature, an integrated framework is provided to capture the features and linkages between refuelling demand and parking patterns across the different study issues and scales. Finally, the paper proposes several open issues that could be explored in future studies from the perspective of integrating parking and refuelling analysis.

The malicious cyberattack in connected automated vehicles leads to a major threat to the safety and security of autonomous driving. Different from traditional approaches which generally require perfect knowledge of system models and state measurements, in this article, a novel learning-based adaptive resilient control framework is proposed to defend against various false data injection attacks on the steering system to improve safe driving for automated vehicles. First, a robust nonlinear state estimation method is developed to provide accurate observation of unmeasured state variables for feedback control with limited onboard sensors. Then, an active model learning approach is proposed to present the vehicle driving behavior under different attacks to improve the dynamic model for state prediction over receding horizon. Finally, a data-driven attack-resilient method is designed to optimize the vehicle motion for autonomous driving. The derived control policy can be adapted to different scenarios for safety. MATLAB/Simulink and CarSim co-simulation platform is applied to evaluate the effectiveness and robustness of the proposed method on state estimation, model learning and accurate tracking control under various attack conditions.

Presents corrections to the paper, (Corrections to “Learning-Based Optimal Adaptive Resilient Control for Safe Autonomous Driving Under Cyberattacks”).

Accurate and stable localization system plays a significant role in safe driving for connected automated vehicles (CAVs). However, the vulnerability from GPS spoofing attacks undermines the security of the localization system, posing great challenges for autonomous driving. In this article, a security-critical study for anomaly detection and defense against GPS attacks for CAVs using vehicle-to-vehicle (V2V) technology is explored to improve the localization for driving safety under cyber-attack. First, a robust learning-based GPS stealthy attack model is designed to generate spoofing GPS signals, which can evade currently widely applied Kalman filter-based localization with$\chi ^{2}$anomaly detector and result in vehicle positioning errors, leading to more potential driving hazards than traditional models. Then a novel detection method for GPS anomaly with V2V communication based on density clustering algorithm is proposed to detect the wrong GPS data effectively. When the GPS attack is detected, the vehicle position is estimated accurately by an innovative cooperative localization (CL) approach with multi-information fusion from neighboring vehicles to defend against the GPS attack. The proposed framework is evaluated with three real-world driving data sets in closed-loop simulation. The results show that the developed attack detection method has the best performance compared to the state-of-the-art methods in terms of detection accuracy and detection timeliness. Furthermore, the CL for attack defense can provide accurate position estimation for the victim vehicle against GPS attack to realize safe autonomous driving, illustrating the effectiveness and robustness in various driving scenarios.

Current trajectory prediction methods for autonomous vehicles commonly rely on deep neural networks, which are vulnerable to adversarial attacks. To enhance the security of trajectory prediction, this paper proposes an anomaly detection method based on generative adversarial networks. Firstly, a novel unsupervised anomaly detection model is proposed, taking into account both temporal and spatial features of trajectories with Long Short-Term Memory. The networks are trained using max-min game theory between the generator and the discriminator to capture the normal driving feature distribution. Furthermore, trajectory data is mapped to the latent space, and the generator reconstructs data from the latent space to compute reconstruction loss, while the discriminator detects trajectory data to calculate discrimination loss. Finally, anomalies are detected using an anomaly score that represents the extent to which the data point deviates from normal behavior and determines whether the trajectory of this segment is anomalous within the time window. We evaluate the method on three public datasets, and experimental results demonstrate its excellent performance under adversarial attacks.

Accurate trajectory prediction is crucial for autonomous vehicles to realize safe driving. Current trajectory prediction approaches generally rely on deep neural networks, which are susceptible to adversarial attacks. To evaluate the adversarial robustness and security of deep-learning-based trajectory prediction models, this paper proposes an adversarial attack method on trajectory prediction using generative adversarial networks (GANs). First, a novel LSTM-based attack trajectory model named Adv-GAN is proposed considering both the temporal and spatial driving features. The networks in Adv-GAN are trained through game learning between the generator and the discriminator to obtain the adversarial trajectories with real driving feature distribution. Furthermore, the generated trajectory is optimized with the vehicle kinematics model for driving feasibility on roads. The derived adversarial attack can lead to considerable deviations in trajectory prediction which affects driving safety for autonomous vehicles. We evaluate the proposed Adv-GAN on three public datasets, and experimental results show the effectiveness with better attack performance compared to a state-of-the-art adversarial attack model.

Reducing traffic accidents and associated casualties is a growing concern for modern human society. The secondary or even chain collisions for an unstable vehicle after an initial impact can result in more hazards and fatalities. Passive safety systems such as airbags and seat belts only provide limited level of protection for vehicle occupants, but cannot prevent collision accidents, while active safety systems usually work before the initial collision. Therefore, it is of great significance to develop dedicated post-impact stability control systems to help vehicles quickly restore stability to mitigate and/or avoid secondary collisions. However, the loss of original nonholonomic constraint property and the nonlinearity and saturation of tire forces due to post-impact sideslip, over-spinning, and drifting motions pose great challenges in controller design. Moreover, how to simulate and analyze the collision process and to further construct a simulation environment is the primary problem to solve for enabling controller development. Also, exploring repeatable, effective and low-cost experiment methods lays the foundation for controller verification. This paper aims to provide an overview of the latest technological advancements in collision modeling, control synthesis, and experimental procedures for post-impact stability control. The advantages and disadvantages of different modeling, control and experimental approaches are compared in succession. Finally, the paper discusses the challenges encountered in existing research and the prospects for post-impact active safety control systems.

The two-stage wireless charging system is used in various wireless charging application because its power factor correction (PFC) rectifier and the inverter are decoupled from each other and the control is more convenient, but the large number of power switches and bulky electrolytic capacitors results in low power density of the system, so a number of single-stage solutions were put forward. In this paper, a novel single-stage wireless charging system without bulky DC capacitors is proposed to replace the two-stage one, which is composed of only six switches and two diodes. Moreover, a phase-shifting (PS) modulation scheme is proposed based on this single-stage topology, which can realize power regulation, PFC, and soft switching simultaneously. Finally, a simulation prototype is built to verify its effectiveness.

Connected automated vehicles (CAVs) share information through vehicular networks; however, cyber-attacks on GPS may cause significant challenges to compromise vehicle security and driving safety. In this paper, a novel approach for GPS attack detection and mitigation is proposed using vehicle-to-vehicle (V2V) communication, which enables vehicles to access and utilize accurate location information for autonomous driving. Instead of directly fusing the location data received from other vehicles, a trust evaluation process with a $\chi$2-detector is developed to identify and isolate potential malicious surrounding vehicles that may send erroneous information into the V2V network. Subsequently, a Bayesian approach is employed to fuse data from GPS, inter-vehicle distance, and bearing angle measurements. A real-time Robust-Random-Cut-Forest based detector is constructed to identify possible GPS attacks for an ego vehicle. When a malicious attack is detected, a novel cooperative positioning method is used to mitigate the impact of the GPS attack based on V2V information. Simulation results demonstrate the performance of the proposed approach in detecting GPS attacks timely and improving the positioning accuracy and robustness of CAVs under different attacks.

System dynamics identification significantly impacts trajectory tracking performance for autonomous driving in a dynamic environment. In this paper, a multi-scenario learning model predictive control (MPC) optimization strategy is proposed to reduce model complexity and improve system generalization and robustness. First, the Gaussian process is simplified to reduce the complexity of the system’s residual model while ensuring the optimization problem’s convexity. Then, a meta-learning based multi-scenario model is proposed through online adjusting weight factors to identify the dynamic characteristics when the vehicle drives in a new scenario. Finally, the developed learning model is integrated into a stochastic MPC framework for robust optimization by considering environmental changes and parameter uncertainties. Simulation results show the efficient performance of our proposed method in terms of model prediction accuracy and trajectory tracking.

Accurate vehicle sideslip angle estimation is crucial for vehicle stability control. In this article, an enabling event-triggered sideslip angle estimator is proposed by using the kinematic information from a low-cost global positioning system (GPS) and an on-board inertial measurement unit (IMU). First, a preliminary vehicle sideslip angle is derived using the heading angle of GPS and the yaw rate of IMU, and an event-triggered mechanism is proposed to eliminate the accumulative estimation error. The algorithm convergence is guaranteed through theoretical deduction. Second, a longitudinal and a lateral vehicle velocity are obtained using the preliminary vehicle sideslip angle and the measured GPS velocity and their kinematic relationship, based on which a multisensor fusion and a multistep Kalman filter scheme are, respectively, presented to realize longitudinal and lateral vehicle velocity estimation. By doing this, the update frequency and estimation accuracy of the vehicle sideslip angle estimate can be further improved to meet the requirement of online implementation. Finally, the effectiveness and reliability of the proposed scheme are verified under comprehensive driving conditions through both hardware-in-loop (HIL) and field tests. The results show that the proposed event-triggered sideslip angle estimator has a mean estimation error of 0.029$^\circ$and of 0.14$^\circ$in the HIL and field tests, exhibiting better estimation accuracy, reliability, and real-time performance compared with other typical estimators.

Personal driving behavior affects vehicle energy consumption as well as driving safety; therefore, driving behavior is key information for electric vehicle (EV) energy management and advanced driver assistance systems. Eco-driving is an efficient way to reduce energy consumption and air pollution. As the basis of driving behavior, limited types of driving event information, as used in several other studies, cannot be used to meet eco-driving evaluation study needs. Complex and inconsistent human-defined rules are not conducive to the establishment of driving events. Hence, it is necessary to establish a driving event classification system with more categories of drive-topics that can present a better linkage between driving behavior and energy consumption. This paper proposes a driving event recognition method. Dynamic Local Minimum Entropy is proposed, and the Latent Dirichlet Allocation algorithm is used to classify different driving events. Drive-topics are proposed which describe driving events more accurately. The data from fifty battery-electric taxis are used to train the algorithm with data collected by the Service and Management Center for EVs, Beijing, in 2018. The relationship between drive-topic and energy consumption is analyzed to demonstrate that driving behavior can be established using drive-topics to support the evaluation of eco-driving for battery-electric vehicles.

In this paper, an event-triggered vehicle-following control scheme for connected and automated vehicles (CAVs) is proposed considering nonideal Vehicle-to- Vehicle communications such as communication delays and packet dropouts. An output-based event-triggered mechanism is employed for reducing computational burden. An Event-Triggered Model Predictive Control (ETMPC) is proposed by combining with a multi-target controller for the lateral and longitudinal vehicle-following control of CAVs. The simulation results demonstrate that the proposed ETMPC can avoid unnecessary optimization implementation, achieving a computational reduction by 61.5% while maintaining the tracking precision compared with a conventional Model Predictive Controller. The proposed control scheme is also capable of being employed in vehicle platoon control.

There is an increasing awareness of the need to reduce the traffic accidents and fatality rates due to vehicle rollover incidents. The accurate detection of impending rollover is necessary to effectively implement vehicle rollover prevention. To this end, a real-time rollover index and a rollover tendency evaluation system are needed. These should give high accuracy and be of a low application cost. In this article, we propose a rollover evaluation system taking lateral load transfer ratio (LTR) as the rollover index with inertial measurement unit as the system input. A nonlinear suspension model and a rolling plane vehicle model are established for the state and parameter estimation. An adaptive extended Kalman filter is utilized to estimate the roll angle and rate, which adjusts noise covariance matrices to accommodate the nonlinear model characteristic and the unknown noise characteristic. In the meantime, the forgetting factor recursive least squares method is utilized to identify the height of the center of gravity. The Butterworth filter is used to filter out the high-frequency noise of the acceleration signal and the index of LTR is accordingly calculated based on the estimation results. The proposed scheme is verified and compared through hardware-in-loop tests. The results show that the developed scheme performs well in a variety of operating conditions.

In this paper, a time-delay neural network (TDNN) is proposed to estimate the sideslip angle under extreme maneuvers for in-wheel-motor-drive electric vehicles (IWMD EVs). The principle component analysis (PCA) method is first utilized for data preprocessing. Then a time delay module is introduced into the neural network model to improve its robustness. The estimated sideslip angle is further filtered by the Kalman filter. Finally, the proposed estimation scheme is verified via the comprehensive hardware-in-loop (HIL) tests, in which the proposed method can achieve high estimation accuracy.

Accurate battery aging prediction is essential for ensuring efficient, reliable, and safe operation of battery systems in electric vehicle application. This article presents a novel battery aging assessment method based on the incremental capacity analysis (ICA) and radial basis function neural network (RBFNN) model. The RBFNN model is used to depict the relationship between battery aging level and its influencing factors based on real-world operation datasets of electric city transit buses. The ICA method together with the Gaussian window (GW) filter method is used to derive the peak values of IC curves which are utilized to represent battery aging levels, and the support vector regression (SVR) method is used in several scenarios for data preprocessing. The considered influencing factors include accumulated mileage of vehicles and initial charging state-of-charge (SOC), average charging temperature, average charging current, and average operating temperature of battery systems. The datasets collected from real-world electric city buses are used for RBFNN model training, validation, and test. The results show that an average prediction error of 4.00% is reached, and the derived model has a confidential interval of 92% with the prediction accuracy of 90%. This work provides insights for battery aging prediction based on massive real-time operation data.

Wireless electric vehicle (EV) charger has become increasingly popular because of its improved convenience and safety and its advantage of smaller green-house gas (GHG) emissions. The power control method of the wireless power transfer (WPT) system has great impact on the performance of the charger. In order to simplify the power electronics converters and improve the reliability of the system closed-loop control, a novel secondary-side power control method by adding a pair of bidirectional switches is presented for the wireless EV charger. The proposed control method, which is applied on a double-side LCC compensated wireless power transfer system, is analyzed based on the fundamental harmonic approximation (FHA) approach. The relationship between the controlled duty cycle of the secondary switches and the output power is derived and discussed. The total harmonic distortion (THD) of the input current is also calculated to estimate the influence of the duty cycle on the primary side. A prototype of the WPT system with the proposed control method has been built and tested, and the confirmatory experiment is also presented to validate the theoretical analysis.

Wireless power transfer (WPT) using double-sided LCC compensation features with a proportional relation between input voltage and output current, which is load independent. It lays a solid base for primary-side DC/DC control. In this paper, a novel switching converter control strategy — one cycle control (OCC) combined with proportion differentiation (PD) is proposed to form a robust, fast and precise control for primary-controlled WPT system. The OCC-PD is compared with PID and conventional OCC using Switching Flow-Graph technique. The dynamic responses of different control strategies applied in buck converter connected with a resistor and with WPT stage as the load are analyzed respectively, which guides the design of the proposed control strategy. Finally, the OCC-PD applied in single-stage buck converter and two-stage primary-controlled WPT system is carried out in Matlab/Simulink environment to verify the theoretical analysis, which confirms the superiorities of the proposed OCC-PD strategy.

In this paper, a direct yaw-moment control (DYC) scheme is proposed for a four-wheel-independently-actuated electric vehicle (FWIA EV). An upper controller is based on a reference model with two degrees of freedom (2-DOF) that generates the desired yaw rate for a sliding mode controller to track so as to improve the vehicle dynamic stability. A torque optimization distribution strategy is designed in the lower controller to allocate the required torques to each in-wheel motor for vehicle stability enhancement. The proposed DYC scheme is implemented in a Carmaker vehicle model and a MATLAB/Simulink control model and evaluated in simulations of a snake-lane-change and a double-lane-change maneuver. The results show that the side slip angle and tire load rate have been reduced, on average, by 2/3 using the DYC system compared with those without control. The cases with DYC also provide better tracking of the desired trajectory and yaw rate with smaller steering angle than those without control.