Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor.

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay.

The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm's potential for practical application.

Coordinated Optimal Control of AFS and DYC for Four-Wheel Independent Drive Electric Vehicles Based on MAS Model.

The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0-5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.

Fast Distributed Model Predictive Control Method for Active Suspension Systems.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles.

A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered.

Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment.

Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.