Performance Index and Identification of the Driver’s Control Parameters

The directional control performance of road vehicles and thus the road safety are influenced by control actions of the human driver and directional responses of the vehicle such as lateral position and orientation errors. The control characteristics of the human driver have been mostly identified through minimization of a composite performance index of selected response measures. The selected measures for automobiles have been mostly limited to the lateral position error ( ) or orientation error ( ) or a combination of the two [7,17,40,78]. This approach of identifying the driver control properties may thus yield an idealized driver model, particularly when the control limits of the human

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driver are not considered in the performance index minimization. The vast majority of the driver models, with only few exceptions, do not consider the driver control limits in the control parameters identification process. The reported driver models thus suggest widely different driver control characteristics that may be considered applicable for particular driving condition considered in individual studies. Table 1.7 summarizes the ranges of commonly used driver control parameters such as preview time, lead and lag time constant, and lateral position and orientation error compensatory gains [8,11,14,17,45]. These clearly show wide ranges of control parameters used in different studies. Only a few studies have reported solution of the minimization problem subject to limit constraints defining the practiced ranges of driver control characteristics [11].

Table 1.7: Range of human driver’s control variables

Control variables Unit Range

Preview time, Tp s 0.10 – 2.50

Lead time constant, TL s 0.05 – 3.00

Lag time constant, TI s 0.02 – 0.80

Lateral position error compensatory gain, Ky rad/m 1e-5 – 1.80

Orientation error compensatory gain , KΨ rad/rad 0.10 – 1.85

The reported vehicle driver models employ widely different control characteristics and performance indices comprising the vehicle path and directional responses together with some measures of demands imposed on drivers' effort. These include the maximum steering angle and its rate, which has been related to the driving effort and drivers' comfort [9,15,17]. Since the majority of the studies have focused on driver models applicable to automobiles or two-axle vehicles, the widely used performance index of lateral position and orientation errors alone would be justifiable. Such a performance index, however, would be inadequate for heavy articulated vehicle combinations where

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the driver perceives additional cues from the vehicle responses. Only few studies have explored driver models for articulated vehicle combinations, which have suggested significance of many additional vehicle responses [9,25]. These employ performance indices comprising weighted function of lateral accelerations, yaw rates and roll angles of the articulated vehicle units; articulation angle and articulation rate; and steer angle and its rate. Yang et al. [16] proposed such a performance index, which was minimized upon consideration of ranges of reported control limits of the human driver including preview time, lead and lag time constants and compensation gains.

1.3 Scope and Objective of the Dissertation

From the literature review, it is apparent that the reported human driver models generally consider the driver as an ideal controller that can readily adjust its driving strategy to adapt to a desired vehicle path with little or no considerations of control limits of the human driver. Furthermore, the vast majority of the reported studies on driver-vehicle interactions focus on automobile drivers. The human driver's control performance is perhaps of greater concern for articulated vehicle combinations, which exhibit significantly lower control limits and may pose relatively greater highway safety risks compared to automobiles. The directional dynamic analyses of such vehicles have been limited either to open-loop steering and braking inputs or simplified path-following driver models.

In the context of interactions between the human driver and articulated vehicles, only a few studies have investigated the coupled driver-vehicle system responses. These generally focus on characterization of driving control requirements through minimization of lateral position error between center of mass (cg) of the tractor and the desired path

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using a single-point preview strategy. A single point preview strategy, however, may lead to unsatisfactory path tracking performance and instability, particularly under high speed directional maneuvers coupled with a relatively short preview distance. Furthermore, the assessments of active safety devices in the vast majority of the studies have been limited to either open-loop simulations or in a closed loop simulations using simple driver models. The contributions of the human driver's control limits to the vehicle stability have been mostly ignored. It is thus desirable to develop more effective driver models for articulated vehicles considering feasible ranges of human driver control limits and multiple-point preview. Such a model could serve as an effective simulations tool for assessing safety dynamics of the coupled driver-vehicle system and provide essential guidance toward developments in driver-assist systems (DAS).

This dissertation research thus aims to develop a two-stage preview strategy, involving a near- and a far-point preview simultaneously, to characterize steering control properties of commercial vehicle drivers. The strategy includes a near and a far preview point targets to describe the driver control of lateral path deviation and vehicle orientation. A human driver model comprising path error compensation and dynamic motions of the limb is subsequently formulated and integrated to a yaw-plane model of an articulated vehicle. The coupled driver-vehicle model is analyzed under an evasive steering maneuver to identify limiting values of the driver control parameters through minimization of a generalized performance index comprising driver’s steering effort, path deviations and selected vehicle states. The performance index is further analyzed to identify relative contributions of different sensory feedbacks, which may provide important guidance for designs of driver-assist systems (DAS).

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