Topic
Vehicle dynamics
About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.
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18 Aug 2011
TL;DR: In this article, a real-time estimation method based on Kalman filter is proposed for calculating loads on the wheels using road profile, which is based on the dynamic response of a vehicle instrumented with available sensors.
Abstract: Knowledge of vehicle dynamic data is essential for the enhancement of active safety systems such as suspensions and trajectory control systems. Vehicle controllability analysis on real roads can be obtained only if valid road profile and tire road friction model are known. With regard to the road profile, this study focuses on a real-time estimation method based on Kalman filter. Besides, this paper presents a method for calculating loads on the wheels using road profile. The proposed method is based on the dynamic response of a vehicle instrumented with available sensors. The estimation process is applied and compared to real experimental data obtained with two inertial methods in real conditions. Experimental results show the accuracy and the potential of the proposed estimation process.
91 citations
91 citations
TL;DR: In this article, the authors provide a comprehensive literature review on the side-slip angle estimation problem, focusing on the most effective and innovative approaches, as well as the advantages and limitations of each technique.
Abstract: Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or “drifting” angle) is defined as the angle between the vehicle’s longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.
91 citations
TL;DR: This work first constructs the k-nearest neighbor-based internet of vehicles in a dynamic manner, then learns the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network, and uses these representations to cluster vehicle trajectories with machine learning methods.
Abstract: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods.
91 citations
TL;DR: In this article, a new VDC system for a four motorized-wheels electric vehicle has been developed, for which the traction of each wheel can be controlled individually, which can improve the vehicle handling and active safety of driver and passengers considerably.
Abstract: It is shown that the vehicle dynamic control (VDC) system can improve the vehicle handling and active safety of driver and passengers considerably. The control of vehicle yaw moment through differential braking, based on the vehicle dynamic state feedbacks, is a traditional way of VDC. In this study, a new VDC system for a four motorized-wheels electric vehicle has been developed, for which the traction of each wheel can be controlled individually. Using this feature, the new VDC system provides the desired tractive force of vehicle and the desired external yaw moment through the integrated control of wheel motors. The structure of the control system is a multilayer type, which has been developed by using independent controllers, designed in accordance with the appropriate theories.
91 citations