Search Results

As part of a system for structural health monitoring, it is required to determine the spatial and temporal distributions of vertical loads arising from heavy trucks driven on flexible bridge structures. An instrumented truck is used to generate the input loads and estimate the load time histories. The truck can carry a range of sensors; however direct measurement of vertical tire loads between the tires and the structure is not considered realistic. The dynamic loads are to be estimated from the sensor outputs. These are affected by both truck and bridge dynamics and these must be accounted for within the load estimation process. Estimation may be susceptible to many factors including static mass distribution, vehicle longitudinal motion, variations in lateral position on the bridge, as well as any surface unevenness.

Suspension design is influenced by many factors, especially by vehicle dynamics performance in ride, handling and durability. In the global automotive industry it is common to “customize” or tune suspension parameters so that a vehicle is more acceptable to a different customer base and in a different driving environment. This paper seeks to objectively quantify certain aspects of tuning via ride optimization, taking account of market differences in road surface spectral properties and loading conditions. A computationally efficient methodology for suspension optimization is developed using stochastic techniques. A small (B-class) vehicle is chosen for the study and the following main suspension parameters are selected for optimization - spring stiffness, damping rate and vertical tire stiffness. The road is characterized as a stationary random process, using scaling and shaping filters representative of comparable roads in India and the USA.

This paper investigates series plug-in hybrid electric vehicle (PHEV) behavior during one-day with synthesized representative one-day missions. The amounts of electric energy and fuel consumption are predicted to assess the PHEV impact on the grid with respect to the driving distance and different charging scenarios: (1) charging overnight, (2) charging whenever possible. The representative cycles are synthesized using the extracted information from the real-world driving data in Southeast Michigan gathered through the Field Operational Tests (FOT) conducted by the University of Michigan Transportation Research Institute (UMTRI). The real-world driving data include 4,409 trips covering 830 independent days and temporal distributions of departure and arrival times. The sample size is large enough to represent real-world driving.

The proposed control provides more longitudinal motion control compared to conventional Vehicle Stability Control (VSC) design consisting of only lateral and yaw motion control. This vehicle motion control is achieved using the model-based hierarchical control, consisting of three layers: (1) vehicle motion control as the upper layer, (2) an intermediate layer for the brake torque distribution using Model Predictive Control (MPC) and (3) a lower layer for the wheel slip control at each wheel. Performance evaluation is conducted using an open-loop brake-in-turn maneuver and vehicle responses are compared to those of the pure braking control using a simply designed ABS to maximize braking force by tracking pre-defined peak slip ratio.

The characterization of human drivers' performance is of great significance for highway design, driver state monitoring, and the development of automotive active safety systems. Many earlier studies are restricted by experimental scope, the number and diversity of human subjects, and the accuracy and extent of measured variables. In this work, driver lateral control performance on limited-access highways is quantified by utilizing a comprehensive naturalistic driving database, with the emphasis on measures of vehicle lateral position and time to lane crossing (TLC). Normative values at various speed ranges are reported. The results represent a statistical view of baseline on-road naturalistic driving performance, and can be used for quantitative studies such as driver impairment and alertness monitoring, the triggering of lane departure warning systems, and highway design.

This paper reviews the field operational test (FOT) methodology adopted in recent years for the evaluation of driver-assistance systems. The Road Departure Crash Warning System program is used both for illustration and as a case study. This project involved an extensive field operational test of a driver-assistance system using volunteers from the general public who drove instrumented research vehicles in place of their normal cars. Objective and subjective data were collected in these trials, and comparisons were made between driving behavior under conditions where the systems were either enabled or disabled. This paper presents sample results from the analyses and draws conclusions on the strengths and weaknesses of the FOT method.