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.

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.

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.

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.

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.

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.