An automated driver based on convergent vector fields

Abstract

This paper describes a new general framework for the action of an automated driver (or driver model ) to provide the control of longitudinal and lateral dynamics of a road vehicle. The context of the problem is assumed to be in high-speed competitive driving, as in motor racing, where the requirement is for maximum possible speed along a track, making use of a reference path (racing line) but with the capacity for obstacle avoidance and recovery from large excursions. While not necessarily representative of a human driver, the analysis provides worthwhile insight into the nature of the driving task and oVers a new approach for vehicle lateral and longitudinal control; it also has applications in less demanding applications such as Advanced Cruise Control systems. As is common in the literature, the driving task is broken down into two distinct subtasks: path planning and local feedback control. In the rst of these tasks, an essentially geometric approach is taken here, which makes use of a vector eld analysis. At each location x the automated driver is to prescribe a vector w for the desired vehicle mass centre velocity; the spatial distribution and global properties of w(x) provide essential information for stability analysis, as well as control reference. The resulting vector eld is considered in the context of limited friction and limited mass centre accelerations, leading to constraints on $ w. Provided such constraints are satis ed, and using suitable adaptation of w(x) when required, it is shown that feedback control can be applied to guarantee stable asymptotic tracking of a reference path, even under limit handling conditions. A speci c implementation of the method is included, using dual non-linear SISO (single-input single-output) controllers.

An adaptive lateral preview driver model

“…Horiuchi et al [20] suggests a multi-input driver model using lateral displacement and yaw errors in feedback form, each driver (controller) having different controller gains and delays. In an alternative approach by Gordon et al [21], the driving behaviour is decomposed into two sequential tasks: path planning and feedback control. The path planning task in [21] is addressed through an infinite state representation of the desired path, in the form of a reference vector field.…”

Dynamics simulation studies on the electric city car with an electromechanical differential and the rear wheels drive

“…The Angle δ A is the replacement angle of setting the front wheels relative to the longitudinal car axis os. The more extended models of the vector control of a car are shown for example in work by [15]. As a measure of the objective functions ∆D it is assumed an average distance of car point A from the given road (setpoint).…”

The relationship between vehicle yaw acceleration response and steering velocity for steering control

“…This model is successfully applied to a Formula One vehicle performing a lane change manoeuvre. Gordon, Best and Dixon (2002) make use of a novel method, based on convergent vector fields, to control the vehicle along desired routes. The vehicle model is a 3-dof vehicle, with nonlinear Magic Formula tyre characteristics, but with no suspension deflection included.…”