Search Results

A combined biodynamic and vehicle model is used to assess the vibration and performance of a human operator performing driving and other tasks. The other tasks include reaching, pointing and tracking by the driver and/or passenger. This analysis requires the coordinated use of separate and mature software programs for anthropometrics, vehicle dynamics, biodynamics, and systems analysis. The total package is called AVB-DYN, an acronym for Anthropometrics, Vehicle and Bio-DYNamics. The biodynamic component of AVB-DYN is described, and then compared with an experiment that studied human operator in-vehicle reaching performance using the U.S. Army TACOM Ride Motion Simulator.

This paper presents an analysis of driver/vehicle performance over a range of maneuvering conditions including accident avoidance scenarios involving vehicle limit performance handling. Driver behavior is considered in the same dynamic analysis terms as vehicle response in order to give appropriate closed-loop measures of total system maneuvering capability and handling stability. A driver control structure is developed along with closed-loop system stability constraints on model parameters over a wide range of vehicle maneuvering conditions. Example simulation runs are presented for several accident avoidance scenarios.

A combined biodynamic and vehicle model is used to assess the vibration and performance of a human operator performing driving and other tasks. The other tasks include reaching, pointing and tracking by the driver and/or passenger. This analysis requires the coordinated use of separate and mature software programs for anthropometrics, vehicle dynamics, biodynamics, and systems analysis. The total package is called AVB-DYN, an acronym for Anthropometrics, Vehicle, and Bio-DYNamics. The objectives and architecture are discussed, and then a preliminary version of this package is demonstrated in an example where a HMMWV (High Mobility Multipurpose Wheeled Vehicle) operator is performing a driving task.

This paper describes the results of an experiment concerning driver behavior in car following tasks. The motivation for this experiment was a desire to understand typical driver car following behavior as a guide for setting the automatic control characteristics of an ACC (Adaptive Cruise Control) system. Testing was conducted under both test track and open road driving conditions. The results indicate that car following is carried out under much lower bandwidth conditions than typical steering processes. Dynamic analysis shows driver time delay in response to lead vehicle velocity change on the order of several seconds. Typical longitudinal acceleration distributions show standard deviations of less than 0.05 g (acceleration due to gravity).

This paper describes the driver/vehicle modeling aspects of a computer simulation that can respond to highway engineering descriptions of roadways. The driver model interacts with a complete vehicle dynamics model that has been described previously. The roadway path is described in terms of horizontal and vertical curvature and cross slopes of lanes, shoulders, side slopes and ditches. Terrain queries are made by the vehicle dynamics to locate tires on the roadway cross-section, and to define vehicle path and road curvature at some distance down the road. The driver model controls steering to maintain lateral lane position. Speed is maintained at a speed limit on tangents, and decreased as needed to maintain safe lateral acceleration. Because the bandwidth of longitudinal (speed) control is much lower than lateral/directional (steering) control, the driver model looks further ahead for speed control than for steering.

Full-scale vehicle response tests were conducted on five vehicles using a crosswind disturbance test facility capable of providing a 35 mph wind over a nominal 120 ft test length. The vehicles were a Honda Accord, Chevrolet station wagon, Ford Econoline van, VW Microbus, and Ford pickup/camper. Results showed that passenger cars, station wagons, and most vans have virtually no crosswind sensitivity problems, whereas the VW Microbus, the pickup/camper (in winds higher than 35 mph), and cars pulling trailers do have potential problems. Key vehicle parameters dictating this yaw response sensitivity are the distance between the aerodynamic and tire force centers, tire restoring moment (including understeer gradient), and the basic aerodynamic side forces. A simple analytical relationship in these terms was developed to predict steady-state yaw rate in steady winds.

Interactive driving simulation is attractive for a variety of applications, including screening, training and licensing, due to considerations of safety, control and repeatability. However, widespread dissemination of these applications will require modest cost simulator systems. Low cost simulation is possible given the application of PC level technology, which is capable of providing reasonable fidelity in visual, auditory and control feel cuing. This paper describes a PC based simulation with high fidelity vehicle dynamics, which provides an easily programmable visual data base and performance measurement system, and good fidelity auditory and steering torque feel cuing. This simulation has been used in a variety of applications including screening truck drivers for the effects of fatigue, research on real time monitoring for driver drowsiness and measurement of the interference effect of in-vehicle IVHS tasks on driving performance.

Models have been developed to describe the dynamic response and performance of drivers, vehicles, and driver-vehicle systems; and recent experiments have provided some quantification and refinement. This paper summarizes the theory and the data, and attempts to provide part of the transition between properties of the human and the assessment of safety performance in driving. The model and data shown emphasize steering or directional control situations. Simulation experiments with random crosswind gust disturbances were used to measure driver-vehicle describing functions for a number of driver subjects and experimental replications. The results are consistent with previous data and show good repeatability within subjects on successive runs. Interpretation of the data in terms of the driver-vehicle model indicates that the driver's outputs can be explained in simplest terms as functions of lateral position and heading.

Interactive simulation requires providing appropriate sensory cuing and stimulus/response dynamics to the driver. Sensory feedback can include visual, auditory, motion, and proprioceptive cues. Stimulus/response dynamics involve reactions of the feedback cuing to driver control inputs including steering, throttle and brakes. The stimulus/response dynamics include both simulated vehicle dynamics, and the response dynamics of the simulation hardware including computer processing delays. Typically, simulation realism will increase with sensory fidelity and stimulus/response dynamics that are equivalent to real-world conditions (i.e. without excessive time delay or phase lag). This paper discusses requirements for sensory cuing and stimulus/response dynamics in real-time, interactive driving simulation, and describes a modest fixed-base (i.e. no motion) device designed with these considerations in mind.

Driver response and performance can be quantified by observing the stimulus-response environment. Yet the driver's inherent adaptability allows him to have seemingly adequate performance in potentially hazardous driving situations even though he may be operating near the acceptable safety limits. Physiological measures of the driver's internal state can provide further quantification of his performance level and can give a measure of his workload or safety performance margin. Measures of driver physiological and control responses have been made under gust disturbance conditions with the subject's car operating at various speeds. The experimental techniques and data are described, and correlations between the situational parameters and driver stress and control response are shown.

Conventional system synthesis involves indirect repetitive analysis, while optimal synthesis techniques yield direct solutions, given prescribed criteria. This paper presents some of the relationships between a conventionally derived optimum system and the corresponding optimal system, using a second order example. Methods for specifying optimal cost functions which result in good systems are explored.

The driver's ability to control the lateral position of an automobile is dependent on his perception of the command path (roadway) to be followed. This perception is affected by both the configuration of road markings and other features, and the visibility of these elements. As visibility decreases, the driver's preview of the commanded path is reduced. Theory indicates that driver performance should degrade with reduced preview and configurational parameters which characterize the intermittent nature of delineation (e.g., dashed lines). This paper describes a simulation experiment in which driver behavior and driver/vehicle system performance were measured over a range of visibility and configuration parameter variations. Driver dynamic response and noise (remnant) were reliably affected by variations in visibility and configuration. These effects were also reflected in system performance measures such as lane deviations.