Zevi Bareket

Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System

This paper addresses the development of driver assistance systems whose functional purposes are to provide both adaptive cruise control (ACC) and forward collision warning (FCW). The purpose of the paper is to combine concepts from human factors psychology, vehicle-dynamics, and control theory, thereby contributing to the body of knowledge and understanding concerning human-centered approaches for designing and evaluating driver assistance systems. Conceptual and experimental results pertaining to driving manually and with the assistance of ACC and FCW are presented. The following human-centered aspects of driver-assistance systems are analyzed and presented: the looming effect; including rule-based and skill-based behavior in the design of ACC systems; using desired dynamics in controlling the driving process; braking rules that trade headway range for deceleration level; and collision-warning rules based on two different stress indicators. Field-test data are examined to justify and verify the parametric values selected for use in human-centered ACC systems. Measured data from on-road driving are used to evaluate the performance of proposed FCW systems in braking situations. The paper concludes with observations concerning the difficulty of developing a clear understanding of when and why drivers brake.

Stochastic Modeling for Studies of Real-World PHEV Usage: Driving Schedule and Daily Temporal Distributions

Daily driving missions provide the fundamental information required to predict the impact of the plug-in hybrid electric vehicle (PHEV) on the grid. In this paper, we propose a statistical modeling approach of daily driving mission sets. The approach consists of temporal distribution modeling and the synthesis of individual representative cycles. The proposed temporal distribution model can capture departure and arrival time distributions with a small number of samples by statistically relating the distributions. Then, representative naturalistic cycles are constructed through a stochastic process and a subsequent statistical analysis with respect to driving distance. They are randomly assigned to the temporal distribution model to build up complete daily driving missions. The proposed approach enables the assessment of the impact on the grid of a large-scale deployment of PHEVs using a small number of simulations capturing real-world driving patterns and the temporal distributions of departure and arrival times.

Evaluating Headway Control Using Range Versus Range-Rate Relationships

SUMMARY This paper uses range versus range-rate diagrams to illustrate concepts concerning the objectives of headway control, linear trajectories in the range versus range-rate space, and the influences of acceleration/deceleration limits on headway control systems. Relationships illustrated in range versus range-rate diagrams are used in evaluating first, second, and higher order systems for automatically controlling headway. Ideas for comparing driver control of headway with automatic control of headway are presented.

Methodology for assessing adaptive cruise control behavior

This paper reports on nonintrusive methods for characterizing the longitudinal performance of vehicles equipped with adaptive cruise control (ACC) systems. It reports the experimental set-up and procedures for measuring ACC system performance, followed by the modeling and simulation of the measured ACC performance. To further assess the interaction of ACC vehicles with human-controlled traffic, microscopic simulation involving both a human-driver model and an ACC model is discussed.

Evolving model for studying driver-vehicle system performance in longitudinal control of headway

A model for studying and evaluating the performance of drivers in controlling headway situations is currently being used to better understand how a driver’s perception of headway range and its rate of change in time (range rate) influence the performance of the driver-vehicle system in freeway driving situations. The model is based upon ideas derived from vehicle dynamics, control theory, and human factors research. It is an interpretive model in the sense that results obtained during real driving are processed to evaluate the parameter values and functional relationships used in the model. In this way, the model evolves as new data and information become available and as calculated results are interpreted and understood.

Learning Drivers’ Behavior to Improve Adaptive Cruise Control

Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers. In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce an approach to combine machine learning algorithms with demographic information and behavioral driver models into existing automated assistive systems. This approach can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This approach sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior exclusively based on the ACC’s sensors, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

EVALUATING THE INFLUENCES OF ADAPTIVE CRUISE CONTROL SYSTEMS ON THE LONGITUDINAL DYNAMICS OF STRINGS OF HIGHWAY VEHICLES

SUMMARY This paper presents experimental results, analytical findings, and simulation evaluations pertaining to the longitudinal dynamics and headway performance of strings of vehicles with and without adaptive cruise control (ACC) systems. It focuses on the amplification of speed disturbances along a string of vehicles, i.e., the stability of string behavior. The work describes measurement, analysis, and simulation tools that are suitable for use in evaluating the impact of ACC system characteristics on traffic flow.

Tests Characterizing Performance of an Adaptive Cruise Control System

This paper describes methods for characterizing the headway control performance of adaptive cruise control (ACC) systems. The inputs to the test are the speed of the preceding vehicle. Results of the tests are based upon measurements of range, range rate, velocity transmission shift commands,, and velocity commands. Numerical performance measures are derived from these data and are used to characterize system performance quantitatively.

PREDICTIVE ANALYSES OF THE PERFORMANCE OF A HEADWAY CONTROL SYSTEM FOR HEAVY COMMERCIAL VEHICLES

This paper presents the results of analyses aimed at examining the performance of a vehicle system consisting of a heavy truck, range and range-rate sensors for measuring the relative motion of a leading vehicle, a cruise control modified to accept velocity commands, and a control unit for switching in and out of a headway-control mode. A control strategy, that approximately transforms the system into an equivalent first order system, is described. Physical interpretations of the meanings of the control system parameters are made using simulation results to illustrate the influences of design choices and measurement errors.

INCLUDING ROADWAY AND TREAD FACTORS IN A SEMI-EMPIRICAL MODEL OF TRUCK TYRES

Abstract A semi-empirical tire model is described here and used in examining the influences of changes in (a) tire tread depth and (b) pavement mean texture depth and skid number on tire forces. Test results show that changes in tread depth lead to changes in cornering stiffness and the form of the pressure distribution. The ways in which these factors are included in the model and their influences on lateral force characteristics of truck tires are presented and discussed. The influences of tread depth, mean texture depth, and skid number on die sliding friction of truck tires are discussed and equations for representing the frictional characteristics of a worn tire operating on a wet surface (not flooded to a level well over the tops of the road asperities) are presented in an appendix. The body of the paper concludes with a discussion of future approaches for representing frictional effects.