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Driver distraction has been deemed a major cause of traffic accidents. However, drivers’ brain response activities to different distraction types have not been well investigated. The purpose of this study is to investigate the response of electroencephalography (EEG) activities to different distraction tasks. In the conducted simulation tests, three secondary tasks (i.e., a clock task, a 2-back task, and a navigation task) are designed to induce different types of driver distractions. Twenty-four participants are recruited for the designed tests, and differences in drivers’ brain response activities concerning distraction types are investigated. The results show that the differences in comprehensive distraction are more significant than that in single cognitive distraction. Friedman test and post hoc two-tailed Nemenyi test are conducted to further identify the differences in band activities among brain regions. The results show that the theta energy in the frontal lobe is significantly higher than that in other brain regions in distracted driving, whereas the alpha energy in the temporal lobe significantly decreases compared to other brain regions. These results provide theoretical references for the development of distraction detection systems based on EEG signals.

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times.

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.

The paper proposes a motion control framework for the unmanned wheel-legged hybrid vehicle to track the motion trajectory considering uncertain disturbances. The whole-body dynamic model is built with the contact force of each rolling wheel, which serves as the foundation to obtain trajectory tracking. The angular momentum and linear momentum are optimized by the robust model predictive control algorithm considering the soft constraint of the relaxation variable. The contact force between wheel and ground is solved by the quadratic programming algorithm to efficiently obtain the flexion/extension joint and wheel motion planning. Then, the explicit algorithm to calculate the torque commands of the flexion/extension joint considering the feed-forward torque and feedback torque to improve the control accuracy. Simulation results validate that the control framework based on the robust model predictive control algorithm can solve the uncertain disturbances in process of the vehicle running on the rough road.

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

This paper presents a novel local motion planning framework in a hierarchical manner for autonomous vehicles to follow a trajectory and agilely avoid obstacles. In the upper layer, a new path-planning method based on the resistance network is applied to plan behaviors (e.g. lane keeping or changing), where the human-like factors can be included to simulate different driver styles, such as the aggressive, moderate, and conservative. The planned results (i.e. the lane-change command and the local planned path) will guide the lower-layer planner to decide the local motion. In the lower layer, for the sake of simplicity and alleviation of the computational burden, two separate model predictive controllers (MPC) based on a point-mass kinematic model are utilized for both longitudinal and lateral motion planning. Finally, a super-twisting sliding mode controller (STSMC) based motion tracker is designed to show the feasibility of the proposed decoupled planning method and decide the desired control actions of autonomous vehicles. Several scenarios are defined to comprehensively test and demonstrate the effectiveness and the real-time applicability of the new motion-planning framework. The results show that the proposed method performs very well in the planning and tracking process and takes less than $\text{25 ms}$ for the whole planning process, which can be easily implemented in real-world applications.

Distributed-drive electric vehicles (EVs) replace internal combustion engine with multiple motors, and the novel configuration results in new dynamic-related issues. This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures (DVAS) for EVs driven by in-wheel motors (IWM). Firstly, a DVAS-based quarter suspension model is developed for distributed-drive EVs, from which nine parameters and five responses are selected for the coupling effect analysis. A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses. The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables, and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints. A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration, and two optimized parameter sets for these two objects are provided at last. Simulation results provide in-depth conclusions for the coupling effects between parameters and responses, as well as a guideline on how to design system parameters for contradictory objectives. It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36% and 15% by properly changing the IWM suspension system parameters.

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim–Simulink simulation.

A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.

This paper presents a novel integrated control framework that includes simultaneous vehicle lateral stabilization and path tracking by considering the combined-slip effect, wheel dynamics, and tire force nonlinearities for driver-assistance systems. Active front steering and brakes are the actuators of the developed slip-aware receding horizon control system, in which the loss of cornering forces caused by longitudinal slip dynamics is considered in the prediction model. The controller monitors tire capacities and normal forces, corrects driver’s input, and adjusts wheel torques and steering to provide safe performance in path tracking (e.g., lane-keeping) while stabilizing the vehicle within its handling limits. The main advantage of the developed slip-aware driver-assistance controller is the ability in handling multi-actuation vehicle systems through its more accurate prediction model. The performance and computational efficiency of the integrated strategy is evaluated in hardware-in-the-loop real-time experiments, in various pure- and combined-slip maneuvers, under different road friction conditions. Stability of the controller and longitudinal tire force observer is also investigated. The real-time experiments and simulations reveal that the proposed control framework outperforms the existing algorithms in dealing with reduced tire capacities in harsh maneuvers as a consequence of simultaneous vehicle and wheel stabilization, and path tracking.

An adaptive neural network (ANN) control method for a continuous damping control (CDC) damper is used in vehicle suspension systems. The control objective is to suppress positional oscillation of the sprung mass in the presence of road irregularities. To achieve this, a boundary model is first applied to depict dynamic characteristics of the CDC damper based on experimental data. To overcome nonlinearity issues of the model system and uncertainties in the suspension parameters, an adaptive radial basis function neural network (RBFNN) with online learning capability is utilized to approximate unknown dynamics, without the need for prior information related to the suspension system. In addition, particle swarm optimization (PSO) technique is adopted to determine and optimize the parameters of the controller. Closed loop stability and asymptotic convergence performance are guaranteed based on Lyapunov stability theory. Finally, simulation results demonstrate that the proposed controller can effectively regulate the chassis vertical position under different road excitations. Furthermore, the control performance is determined to be better than that of the typical Skyhook controller.

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to determine road classification output. The most representative semi-active control strategy, i.e. skyhook control, is used to validate this method, and simulation results with varying conditions including different control parameters and sprung mass are compared. The results show that unsprung mass acceleration is most suitable for road classification, and more robust to varying conditions in comparison to other responses.

This paper presents a new method to address issues associated with vehicle system state estimation using an unscented Kalman filter (UKF) with considering full-car system and nonlinear tire force under various international standards organization (ISO) road conditions. Due to the fact that practical road information is complex and noise covariance cannot be treated as a constant, the influence of varying vehicle system process noise variance and measurement noise covariance on the estimation accuracy of the UKF is first discussed. To precisely estimate road information, a novel road classification method using measured signals (vertical acceleration of sprung mass and unsprung mass) of vehicle system is proposed. According to road excitation levels, different road process variances are defined to tune the vehicle system’s variance for application of UKF. Then, road classification and UKF are combined to form an adaptive UKF (AUKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under varying road conditions. Simulation results reveal that the proposed AUKF algorithm has higher accuracy for state estimation of a vehicle system under various ISO road excitation condition.

This study investigates adaptive neural network (NN) state feedback control and robust observation for an active suspension system that considers parametric uncertainties, road disturbances and actuator saturation. An adaptive radial basis function neural network is adopted to approximate uncertain non-linear functions in the dynamic system. An auxiliary system is designed and presented to deal with the effects of actuator saturation. In addition, since it is difficult to obtain accurate states in practice, an NN observer is developed to provide state estimation using the measured input and output data of the system. The state observer-based feedback control parameters with saturated inputs are optimised by the particle swarm optimisation scheme. Furthermore, the uniformly ultimately boundedness of all the closed-loop signals is guaranteed through rigorous Lyapunov analysis. The simulation results further demonstrate that the proposed controller can effectively suppress car body vibrations and offers superior control performance despite the existence of non-linear dynamics and control input constraints.

In this paper, a review of the literature on vehicle suspension control with an emphasis on learning based algorithms is presented. An elaborated discussion on the potentials of learning based controllers is also given. Some of the most well-known active and semi-active suspension controllers are elicited from the literature, and their pros and cons are reported. By categorizing the existing suspension control techniques and considering their functionalities, it is tried to make a ground for indicating the high potential of learning strategies for this task. In this context, several advantageous features of statistical learning and computational intelligence methods are enumerated, which can play a key role in the future of vehicle suspension control. In the authors’ view, the necessity of considering learning strategies for suspension control lies in the fact that, over the past two decades, tremendous effort has been exerted on developing smart and autonomous vehicles to reduce the need for human-machine interaction. Given the fact that the final goal of automotive industrialists is to design efficient and safe autonomous vehicles, it is impossible to neglect the pivotal role of probabilistic artificial intelligence and statistical learning algorithms. In short, by reading this review paper, one can find out (1) the state-of-the-art of the conducted researches on suspension controllers, (2) the potential of learning strategies and their applicability to vehicle suspension control, and (3) the important open questions which deserve further investigation to come up with robust, stable and accurate learnable controllers. The outcome of this paper can be of use for practitioners working on designing smart and autonomous vehicles.

A new road estimation based suspension hybrid control strategy is proposed. Its aim is to adaptively change control gains to improve both ride comfort and road handling with the constraint of rattle space. To achieve this, analytical expressions for ride comfort, road handling, and rattle space with respect to road input are derived based on the hybrid control, and the problem is transformed into a MOOP (Multiobjective Optimization Problem) and has been solved by NSGA-II (Nondominated Sorting Genetic Algorithm-II). A new road estimation and classification method, which is based on ANFIS (Adaptive Neurofuzzy Inference System) and wavelet transforms, is then presented as a means of detecting the road profile level, and a Kalman filter is designed for observing unknown states. The results of simulations conducted with random road excitation show that the efficiency of the proposed control strategy compares favourably to that of a passive system.

The camera always suffers from image instability on the moving vehicle due to the unintentional vibrations caused by road roughness. This paper presents a novel adaptive neural network based on sliding mode control strategy to stabilize the image captured area of the camera. The purpose is to suppress vertical displacement of sprung mass with the application of active suspension system. Since the active suspension system has nonlinear and time varying characteristics, adaptive neural network (ANN) is proposed to make the controller robustness against systematic uncertainties, which release the model-based requirement of the sliding model control, and the weighting matrix is adjusted online according to Lyapunov function. The control system consists of two loops. The outer loop is a position controller designed with sliding mode strategy, while the PID controller in the inner loop is to track the desired force. The closed loop stability and asymptotic convergence performance can be guaranteed on the basis of the Lyapunov stability theory. Finally, the simulation results show that the employed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.

Considering the importance of increasing driving safety, the study of safety is a popular and critical topic of research in the vehicle industry. Vehicle roll behavior with sudden steering input is a main source of untripped rollover. However, previous research has seldom considered road excitation and its coupled effect on vehicle lateral response when focusing on lateral and vertical dynamics. To address this issue, a novel method was used to evaluate effects of varying road level and steering wheel input on vehicle roll behavior. Then, a 9 degree of freedom (9-DOF) full-car roll nonlinear model including vertical and lateral dynamics was developed to study vehicle roll dynamics with or without of road excitation. Based on a 6-DOF half-car roll model and 9-DOF full-car nonlinear model, relationship between three-dimensional (3-D) road excitation and various steering wheel inputs on vehicle roll performance was studied. Finally, an E-Class (SUV) level car model in CARSIM® was used, as a benchmark, with and without road input conditions. Both half-car and full-car models were analyzed under steering wheel inputs of 5°, 10° and 15°. Simulation results showed that the half-car model considering road input was found to have a maximum accuracy of 65%. Whereas, the full-car model had a minimum accuracy of 85%, which was significantly higher compared to the half-car model under the same scenario.