Shared control provides a human-centered development direction for intelligent driving. However, existing shared control methodologies address the risk factors related to both the driver and the traffic environment inadequately. To this end, a shared steering control strategy is proposed based on the driver-vehicle-road (DVR) system risk assessment result. Firstly, the driver’s steering behavior is described through a two-point preview driver model. The key parameters are identified using real driving data. Meanwhile, the deep deterministic policy gradient (DDPG) algorithm is applied to train a reinforcement learning (RL) agent considering tracking accuracy, steering smoothness and vehicle stability as the autonomous driving controller. Afterwards, three time-varying risk factors are designed to evaluate the DVR system risk level, which represent driver risk, road risk and lane departure risk, respectively. Based on the system risk level, the control authority is initially calculated by a fuzzy inference method. Then, considering the smoothness of authority transition, a model prediction control (MPC) method is applied to optimize the initial authority level in real-time. Finally, simulation and the driver-in-the-loop (DIL) experiments are performed to validate the proposed strategy. The results demonstrate that the proposed shared control strategy could reduce driving burden and demonstrates distinct superiority in terms of human-machine collaboration, driving comfort and personalized support.

The existing research on intelligent driving vehicles mainly focuses on improving the performance of safety, economy, and control accuracy, ignoring the personalized manipulation pReferences of different driving groups. The differences in driving styles and preferences of different passengers require that the driving behavior of intelligent driving systems in different traffic situations should conform to the habits of self-vehicle passengers, that is, to achieve personalized driving considering human factors. This paper provides a comprehensive and systematic review of the research status in the field of personalized driving. Firstly, it clarifies the necessity of personalized driving. Secondly, the existing decision-making and planning methods for personalized driving of single-vehicle are summarized from two aspects: machine learning-based methods and driver characteristic characterization-based methods. On this basis, the interactive decision-making and planning method of multi-vehicle games considering personalized preference in intelligent networking and mixed driving environments is summarized. Finally, the problems faced by the research of personalized intelligent driving systems and the future development trend are analyzed and prospected.

With the rapid advancement of end-to-end autonomous driving, the integration of prediction and planning has increasingly become a research focus in the field of autonomous driving. However, most existing methods do not adequately consider the robustness of driving trajectories during the trajectory generation, making them less effective in handling complex driving scenarios. To address this issue, this paper introduces Planning with Path-Informed Prediction for Autonomous Driving (PPP), which constructs a prediction-decision module that fuses multi-dimensional information by integrating the ego vehicle's potential multimodal future paths with environmental features. Moreover, we introduce a multi-stage trajectory evaluation mechanism during the trajectory generation process, which significantly enhances the system's performance in dynamic environments, thereby achieving improvements in both accuracy and robustness in complex driving scenarios. Through experiments on the nuPlan dataset, our method demonstrates exceptional competitiveness in closed-loop tests. Notably, in complex scenario tests, PPP outperforms learning-based and hybrid methods. Code will be available under https://github.com/Keria0812/PPP.

Reinforcement learning (RL) offers a powerful paradigm for adaptive motion planning in complex driving environments. However, applying RL to autonomous driving remains challenging due to uncertainty from partial observability and the stochastic, multimodal behaviors of traffic participants. This paper presents a novel risk-aware RL framework for non-conservative motion planning under uncertainty. By integrating Partially Observable Markov Decision Processes (POMDP) with a deep RL-based policy optimization scheme, the proposed approach explicitly models aleatoric uncertainty via a Gaussian Mixture Bayesian Belief Updater and a time-varying risk field. Additionally, an Adaptive Context-aware Attention (ACA) module is employed to prioritize critical targets for enhanced interaction modeling dynamically. Extensive experiments on the CARLA simulator show that the framework generalizes well across diverse traffic conditions, improving average reward by 65.74% and 64.02% in low-speed dense and high-speed sparse scenarios. It remains robust in challenging situations such as overtaking and sudden lane changes in the PeMS dataset. Furthermore, distributed deployment tests confirm a real-time performance of 10 Hz on a hardware-in-the-loop platform, demonstrating the feasibility of practical deployment.

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.

In this paper, a safety-oriented hierarchical personalized driving system is proposed, which aims to mitigate the preference conflict between the passengers and the intelligent vehicle control system. Firstly, experiments on driving simulator are designed to analyze both the general and individual characteristics of different drivers, and a driving risk field (DRF) model for various driving events, such as free-driving, car-following, and lane-changing, is constructed. Secondly, the HighD natural dataset is clustered to explore the real preferences of different driving styles, and the DRF is calibrated to describe the driver’s subjective risk feeling more realistically. Thirdly, a driving decision-making mechanism with consideration of safety, efficiency, and personalized tolerance on the current lane is designed to select optimal driving events. Then, multi-point visual preview longitudinal speed adjustment and lateral lane-changing trajectory planning methods based on the spatial-temporal DRF under different driving events are proposed. Finally, human-in-the-loop experiments show that the proposed real-time system can generate personalized trajectories for different passengers in changing environments.

Trajectory prediction (TP) of surrounding vehicles (SVs) is crucial for autonomous vehicles (AVs) to understand traffic situations and achieve safe-efficient decision-making and motion planning. However, different drivers’ personalized driving preferences will bring uncertainties for long-term TP in the mixed traffic environment. To this end, this paper proposes a TP model with interaction awareness and driving style awareness for long-term TP of heterogeneous SVs. Firstly, the driving conditions in the highD dataset are distinguished, and three different driving styles of the vehicle in the car-following condition are obtained based on an unsupervised clustering algorithm. Then, an encoder-decoder architecture based on novel lane attention and multi-head attention mechanisms is proposed, where the encoder analyzes historical trajectory patterns and the decoder generates future trajectory sequences. The lane attention mechanism enhances the spatial perception capability of vehicles towards the target lane, and the multi-head attention mechanism extracts high-dimensional global interaction information about the heterogeneous vehicle group (HVG) surrounding the target vehicle (TV). Experimental results show that the proposed model outperforms state-of-the-art models in root-mean-square-error (RMSE) for long-term TP and exhibits excellent adaptability to diverse driving tasks. Moreover, this paper verifies that the driving style topology within the HVG has multiple impacts on the TP accuracy of the TV.

This paper proposed a set of steering control strategies for commercial vehicles based on Electro-Hydraulic Coupling Steering (EHCS) system, including power assistance control and active front wheel steering control. Firstly, the dynamic model of EHCS was established. Then, steering assistance control strategy and active front wheel steering control strategy were respectively designed based on the torque mode and angle mode of the power steering motor, and validated in the Simulink simulation environment. Finally, real-road tests were conducted on a test vehicle equipped with EHCS, and the smoothness and agility of EHCS control strategies were verified through subjective evaluation by test drivers combined with whole vehicle tests.

Human-driven vehicles (HDVs) and autonomous vehicles will coexist for a long time. The time-varying charac-teristics of human-driven vehicles need to be considered when designing cruise strategies for autonomous vehicles. In this paper, the variable forgetting factor recursive least squares (VFFRLS) is proposed to identify the characteristic parameters of HDV. Based on the obtained characteristic parameters, the influence of the HDV on the stability of the vehicle platoon is analyzed, and the optimal time headway of the following autonomous vehicle is selected. Then, the time-varying cooperative adaptive cruise control method is designed to reduce the acceleration perturbation caused by HDV. Based on the data collected by the driving simulator, it is verified that the parameter identifi-cation method proposed in this paper can effectively extract the driving characteristics of HDV. Finally, the numerical simulation results demonstrate that the control strategy enhances vehicle platoon stability and improves traffic efficiency in mixed traffic environments.

Fatigue and distraction are the most common long-term poor state and short-term abnormal behavior of drivers, significantly increasing the driving risk of vehicles equipped with the advanced driver assistance system (ADAS). To provide a more reliable decision-making basis for ADAS and improve driving safety, this paper proposes a driving risk assessment framework considering the driver’s long-term poor state and short-term abnormal behavior. Firstly, based on the self-built fatigue dataset and transfer learning method, an adaptive fatigue detection model with strong generalization capability is established to enable multi-view driver fatigue detection. Then, the idea of multi-clustering and adding offset parameters is introduced into the classical contrast loss function, and the D-InfoNCE loss function is designed to realize the accurate identification of the driver’s specific distraction behavior under open set detection. Subsequently, a driving risk assessment system is developed to quantify driving risk based on the vehicle driving risk factors when fatigued or distracted driving occurs. Finally, the proposed driving risk assessment system is validated by the datasets and driver-in-the-loop test bench. The results show that the proposed framework can accurately detect the driver’s fatigue state and distraction behavior and give ADAS the corresponding driving risk levels to enhance driving safety.

Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. The experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.

Heterogeneous vehicle platoons, consisting of a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers, present a promising solution to address various challenges arising from fully autonomous driving. In this paper, we propose a novel LSTM-based distributed model predictive control (DMPC) platooning method. Initially, we develop and train a vehicle acceleration prediction model based on a long short-term memory (LSTM) network using real-world driving data. Subsequently, the predicted acceleration sequence of the leading HDV is integrated into the DMPC-based platoon control model for the following CAVs. To validate the effectiveness of our method, we conduct simulation experiments using real-world driving data. The results demonstrate that, with a time headway of 1 s, the maximum speed error and maximum spacing error of the heterogeneous vehicle platoon using the proposed LSTM-based DMPC are reduced by at least 5.8% and 5.9%, respectively, compared to the traditional DMPC method. Furthermore, the LSTM-based DMPC outperforms the Transformer-based DMPC method, resulting in a 1.0% reduction in maximum speed error and a 0.7% reduction in maximum spacing error. The proposed method effectively dampens oscillation caused by the leading HDV and enhances tracking accuracy.

Lane-changing is a critical issue for autonomous vehicles (AVs), especially in complex environments. In addition, different drivers have different handling preferences. How to provide personalized maneuvers for individual drivers to increase their trust is another issue for AVs. Therefore, a framework of human-like path planning is proposed in this paper, considering driver characteristics of visual-preview, subjective risk perception, and degree of aggressiveness. In the decision making module, a model is built to select the most suitable merging spot, with respect to safety factors and the driver’s degree of aggressiveness. And a novel environmental potential field (PF) suitable for arbitrary road structures is designed to describe the driver’s individual risk perception. In the trajectory planning module, a model predictive control (MPC) based path planner is designed according to the decisions in coincidence with the driver’s individual intentions of collision avoidance. Simulation results have demonstrated that the proposed path planner can provide with personalized trajectories for different combinations of driver preferences and steering characteristics, in scenarios of curved roads with different risks of collision.

This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system.

A driver-vehicle-road (DVR) model based on kinematic vehicle model is proposed in this paper. In this DVR model, the kinematics vehicle-road model is adopted, and the driver model considering the human driver's characteristics is also included. Thus the behaviors of human driver's preview and neuromuscular delay can be considered in design of path planner and controller by using this DVR model. The repulsive force field based on the artificial potential field (APF) and the circle decomposition of vehicle shape are used to describe the constraints of obstacle avoidance and the road departure avoidance. Based on the proposed DVR model, a trajectory planer using model predictive control (MPC) is designed with consideration of collision and lane-departure avoidance, driver's intention, and vehicle occupant comfort. Simulation results show that with the proposed planner, the vehicle can successfully avoid static/moving obstacles and return to the original lane without lane departure. Simulation results indicate that the proposed kinematic vehicle model based DVR model can be used to design the path planner in normal driving and some typical driving scenarios. And the proposed path planner can provide the vehicle driven by different human drivers with individually safe trajectories in typical scenarios of obstacle avoidance.

The fuzzy full-order dynamic output-feedback steering assistance control is proposed in this paper to follow large-curvature path for the steer-by-wire (SBW) vehicle. The driver-vehicle-road (DVR) model to follow large-curvature path is built under the assumption that the near and far vision information ofthe road for guidance is considered by the human driver. Five parameters describing the driver's steering characteristics and behaviors are considered as uncertainties of the DVR models with different drivers. The Takagi-Sugeno (T-S) fuzzy model is applied to handle these uncertainties in designing the dynamic output-feedback parallel distributed compensator (DPDC). The compensator design is then reduced to solving several linear matrix inequalities (LMIs). Simulation results show that the proposed controller can provide different human drivers with individual steering assistance in following the large-curvature path, and can reduce the driver's physical and mental workloads.

In this paper, a gain-scheduling, robust, and shared controller is proposed to assist drivers in tracking vehicle reference trajectory. In the controller, the driver steering parameters such as delay time, preview time, and steering gain are assumed to be varying with respect to the different characteristics of drivers, vehicle states, and driving scenarios. Meanwhile, the modeling errors and uncertainties in the tire cornering stiffness are also considered in the driver-vehicle system model and the controller design. A global objective function, considering the tracking error, the driver's physical and mental workloads, and the control effort, is designed to optimize the overall performance of the driver-vehicle system. Constraint on eigenvalue placement is added to the controller design to improve the performance of the closed-loop driver-vehicle system. Simulation results under different maneuvers show that the controller can significantly improve the system performance and reduce the driver's workloads. The controller can reduce the delay time of the driver-vehicle system in emergency maneuvers, particularly for inexperienced drivers.