Vehicle dynamics

About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.

Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

Abstract: To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver's expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver's path-following characteristics, reducing the driver's workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the driver's physical workload and mental workload by about 35% and 50%, respectively, when following the given path.

A Reliable Fusion Positioning Strategy for Land Vehicles in GPS-Denied Environments Based on Low-Cost Sensors

Abstract: How to achieve reliable and accurate positioning performance using low-cost sensors is one of the main challenges for land vehicles. This paper proposes a novel fusion positioning strategy for land vehicles in GPS-denied environments, which enhances the positioning performance simultaneously from the sensor and methodology levels. It integrates multiple complementary low-cost sensors not only incorporating GPS and microelectromechanical-based inertial measurement unit, but also a “virtual” sensor, i.e., a sliding-mode observer (SMO). The SMO is first synthesized based on nonlinear vehicle dynamics model to estimate vehicle state information robustly. Then, a federated Kalman filter (FKF) is designed to fuse all sensor information, which can easily isolate and accommodate such sensor failures as GPS ones due to its decentralized filtering architecture. Further, a hybrid global estimator (HGE) is constructed by augmenting the FKF with a grey predictor, which has the advantages of dealing with the systems with uncertain or insufficient information. The HGE works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outage to realize accurate and reliable positioning. The experimental results validate the effectiveness and reliability of the proposed strategy.

A Study on Straight-Line Tracking and Posture Control in Electric Bicycle

Abstract: The development of automatic control for driving a bicycle is a challenging theme and is expected to be a human assist system. Previously, an acceleration-based method for stabilizing bicycle posture was proposed by the authors. In the experiments with this controller, the posture of the bicycle might be stabilized, but it is impossible to run on the desired trajectory, because there is no consideration with respect to a trajectory control. For the sake of expanding this system into more sophisticated equipment, a realization of the trajectory control for the bicycle is important. From the viewpoint of an assist system for human motion, a unified control of posture and trajectory brings a sophisticated function to a bicycle, and a high-performance bicycle is expected to be a convenient vehicle, similar to a small car. This paper proposes two strategies to stabilize bicycle posture and trajectory control that realizes a straight-line tracking: one is a lateral velocity controller, and the other is a steering function controller. The validity of the proposed approaches is evaluated by simulations and experiments.

Using a Head-up Display-Based Steady-State Visually Evoked Potential Brain–Computer Interface to Control a Simulated Vehicle

Abstract: In this paper, we propose a new steady-state visually evoked potential (SSVEP) brain–computer interface (BCI) with visual stimuli presented on a windshield via a head-up display, and we apply this BCI in conjunction with an alpha rhythm to control a simulated vehicle with a 14-DOF vehicle dynamics model. A linear discriminant analysis classifier is applied to detect the alpha rhythm, which is used to control the starting and stopping of the vehicle. The classification models of the SSVEP BCI with three commands (i.e., turning left, turning right, and going forward) are built by using a support vector machine with frequency domain features. A real-time brain-controlled simulated vehicle is developed and tested by using four participants to perform a driving task online, including vehicle starting and stopping, lane keeping, avoiding obstacles, and curve negotiation. Experimental results show the feasibility of using the human “mind” alone to control a vehicle, at least for some users.

Predictive Energy Management Strategy for Fully Electric Vehicles Based on Preceding Vehicle Movement

Abstract: This paper presents an energy-efficient and terrain-information-and-preceding-vehicle-information-incorporated energy management strategy for fully electric vehicles (FEVs) equipped with in-wheel motors. Saving driving energy with terrain preview and preceding vehicle movement prediction are crucial to prolong the driving distance for an FEV. Unlike conducting energy optimization under the assumption that the preceding vehicle movements are already known in most studies, the front vehicle movements are predicted during each control cycle based on the vehicle-to-vehicle communication, and the FEV vehicle velocity and motor torque distribution are optimized by a nonlinear model predictive controller to reduce energy consumption. The energy-saving objective is achieved by including, in the cost function, the motor energy consumption in each control cycle, while the safety objective is accomplished by keeping a suitable relative distance from the preceding vehicle. Since the nonlinear vehicle longitudinal model is applied, the gridding initial torque plane is utilized in each time step to search for the global minimum. Simulation results show that this method has a better energy-saving performance than the control method without using the preceding vehicle movement information, and the algorithm proposed here has a wide applicability under various driving conditions.