Vehicle dynamics

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

Cyber-Physical Control for Energy-Saving Vehicle Following With Connectivity

Abstract: This article aims to develop an optimal look-ahead control framework to maximize car-following fuel economy, while fulfilling requirements of intervehicle safety Three original contributions make this work distinctive from the existing relevant literature First, a model predictive fuel-optimal controller is constructed to optimize the vehicle speed and continuously variable transmission (CVT) gear ratio The controller leverages state trajectories of the leading vehicle transmitted via Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I) communication How operating conditions affect the engine efficiency and CVT efficiency is explicitly taken into account Second, the controller is sufficiently evaluated in a variety of traffic flows, such as cruising, urban, and highway-like driving, and is compared with a short-sighted alternative without V2V/V2I connectivity Finally, we further demonstrate the advantages of the proposed scheme by a comparison with two existing benchmark controllers

Development of a Six-Degree of Freedom Simulation Model for the REMUS autonomous Underwater Vehicle

Abstract: Describes the development and verification of a six degree of freedom, non-linear simulation model for the REMUS AUV, the first such model for this platform. In this model, the external forces and moments resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the control inputs of the vehicle propeller and fins are all defined in terms of vehicle coefficients. The paper briefly describes the derivation of these coefficients. The equations determining the coefficients, as well as those describing the vehicle rigid-body dynamics, are left in non-linear form to better simulate the inherently non-linear behavior of the vehicle. Simulation of the vehicle motion is achieved through numeric integration of the equations of motion. The simulator output is then verified against vehicle dynamics data collected in experiments performed at sea. The simulator is shown to accurately model the motion of the vehicle. The paper concludes with recommendations for future model validation experiments.

Design of a Nonlinear Observer for Vehicle Velocity Estimation and Experiments

Abstract: This brief presents a nonlinear observer for estimating the longitudinal and lateral velocities based on Dugoff's tire model and vehicle dynamics. The observer has a fixed gain structure and can make full use of information about acceleration measurements and nonlinear vehicle model. A sufficient condition is derived to guarantee the stability of the observer, and the robustness of the observer with respect to additive disturbances is analyzed with the help of input-to-state stability theory. The performance of the observer is compared with that of existing approaches and evaluated experimentally under a variety of maneuvers and road conditions.

A single-wheel, gyroscopically stabilized robot

Abstract: We are developing a novel concept for mobility, and studying fundamental research issues on dynamics and control of the mobile robot. The robot, called Gyrover, is a single-wheel vehicle with an internal gyroscope that provides mechanical stabilization and steering capability. This configuration conveys significant advantages over multi-wheel, statically stable vehicles, including good dynamic stability and insensitivity to attitude disturbances; high manoeuvrability; low rolling resistance; ability to recover from falls; and amphibious capability. In this paper we present the design, analysis and implementation of the robot, as well as the associated research issues and potential applications.

A Framework for Estimating Driver Decisions Near Intersections

Abstract: We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.