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Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends.

Ride control of military vehicles is challenging due to varied terrain and mission requirements such as operating weight. Achieving top speeds on rough terrain is typically considered a key performance parameter, which is always constrained by ride discomfort. Many military vehicles using passive suspensions suffer with compromised performance due to single tuning solution. To further stretch the performance domain to achieving higher speeds on rough roads, semi-active suspensions may offer a wide range of damping possibilities under varying conditions. In this paper, various semi-active control strategies are examined, and improvements have been made, particularly, to the acceleration-driven damper (ADD) strategy to make the approach more robust for varying operating conditions. A seven degrees of freedom ride model and a quarter-car model were developed that were excited by a random road process input modeled using an auto-regressive time series model.

The road profile has been shown to have significant effects on various vehicle conditions including ride, handling, fatigue or even energy efficiency; as a result it has become a variable of interest in the design and control of numerous vehicle parts. In this study, an integrated state estimation algorithm is proposed that can provide continuous information on road elevation and profile variations, primarily to be used in active suspension controls. A novel tire instrumentation technology (smart tire) is adopted together with a sensor couple of wheel attached accelerometer and suspension deflection sensor as observer inputs. The algorithm utilizes an adaptive Kalman filter (AKF) structure that provides the sprung and unsprung mass displacements to a sliding-mode differentiator, which then yields to the estimation of road elevations and the corresponding road profile along with the quarter car states.

Intersection crashes are a frequent and dangerous crash mode in the U.S. Emerging Intersection Advanced Driver Assistance Systems (I-ADAS) aim to assist the driver to mitigate the consequences of vehicle-to-vehicle crashes at intersections. In support of the design and evaluation of such intersection assistance systems, characterization of the road, environment, and drivers associated with intersection crashes is necessary. The objective of this study was to characterize intersection crashes using nationally representative crash databases that contained all severity, serious injury, and fatal crashes. This study utilized four national crash databases: the National Automotive Sampling System, General Estimates System (NASS/GES); the NASS Crashworthiness Data System (CDS); and the Fatality Analysis Reporting System (EARS) and the National Motor Vehicle Crash Causation Survey (NMVCCS).

The availability of active safety systems, such as Lane Departure Warning (LDW), has recently been added as a rating factor in the U.S. New Car Assessment Program (NCAP). The objective of this study is to determine the relevance of the NCAP LDW confirmation test to real-world road departure crashes. This study is based on data collected as part of supplemental crash reconstructions performed on 890 road departure collisions from the National Automotive Sampling System, Crashworthiness Data System (NASS/CDS). Scene diagrams and photographs were examined to determine the lane departure and lane marking characteristics not available in the original data. The results suggest that the LDW confirmation test captures many of the conditions observed in real-world road departures. For example, 40% of all single vehicle collisions in the dataset involved a drift-out-of-lane type of departures represented by the test.

A communication system that uses roadside beacons to broadcast road and traffic information and private messages to vehicles has been developed. The system, called Road/Automobile Communication System (RACS), was the result of a joint research project involving the Public Works Research Institute and 25 private-sector corporations. This paper contains an outline of RACS and of an onboard system developed by TOYOTA and presents the results of field tests conducted in the Tokyo area. The results not only verify the capability of the RACS system and the effectiveness of the in-vehicle equipment but also indicate the potential of such a beacon based network to improve traffic jam and driving safety whilst providing enhanced communication facilities without increasing radio-wave congestion.

Detergent additives in automotive gasoline fuel are mainly designed to reduce deposit formation on intake valves and fuel injectors, but it has been reported that some additives may contribute to CCD formation. Therefore, a standardized bench engine test method for CCDs needs to be developed in response to industry demands. Cooperative research between the Petroleum Association of Japan (PAJ) and the Japan Automobile Manufacturers Association, Inc. (JAMA), has led to the development of a 2.2L Honda engine dynamometer-based CCD test procedure to evaluate CCDs from fuel additives. Ten automobile manufacturers, nine petroleum companies and the Petroleum Energy Center joined the project, which underwent PAJ-JAMA round robin testing. This paper describes the CCD test development activities, which include the selection of an engine and the determination of the optimum test conditions and other test criteria.

Map matching determines which road a vehicle is on based on inaccurate measured locations, such as GPS points. Simple algorithms, such as nearest road matching, fail often. We introduce a new algorithm that finds a sequence of road segments which simultaneously match the measured locations and which are traversable in the time intervals associated with the measurements. The time constraint, implemented with a hidden Markov model, greatly reduces the errors made by nearest road matching. We trained and tested the new algorithm on data taken from a large pool of real drivers.

This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle's past trips. We show that a large portion a typical driver's trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver's current trip with one of the set of previously observed trips. Rather than predicting upcoming road segments, our focus is on making long term predictions of the route. We evaluate our algorithms using a large corpus of real world GPS driving data acquired from observing over 250 drivers for an average of 15.1 days per subject. Our results show how often and how accurately we can predict a driver's route as a function of the distance already driven.

A full vehicle multi-body dynamic (MBD) model with suspension control system is developed for fatigue life prediction under rough road condition. The model consists of tires, a trimmed body, heavy attached parts, powertrain, suspension, joints, and a driver model, and includes a suspension control system that varies characteristics of the suspension according to the rough road inputs. For tires, a commercial MBD tire model is employed with identifiable parameters. The models are simulated to run on the optically measured road surface of the proving ground. Apart from the trimmed body, several important heavy attached parts are modeled separately, that represent dynamic behavior that induces complex body input load. These parts, along with suspension and powertrain systems are connected to the body using nonlinear elements such as joints, springs, and dampers. Contact conditions are used to represent mount bushing, hood lock, stopper rubber, etc.

This study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

Intelligent convoy consisting of heavy duty vehicles is an implementation of IVHS believed to be one of the most practicable proposals to come into reality in the near feature. Control Strategy in the context of Autonomous Intelligent Vehicle Platoon is different from that in other “Lane-keeping” IVHS systems which have been well studied. In this paper, an Autonomous Platoon consisting two articulated commercial vehicles is studied and a model of tractor-trailer type commercial vehicles suitable for control studies is derived based on a single track three-axle bicycle model. The authors give perspectives on the implementation of intelligent convoy of articulated vehicles emphasizing safety issues in emergency situations, as opposed to normal following of the lead vehicle. An initial integrated braking and steering control is developed to avoid spinout or jack-knifing when specific axles are locked during braking process.

Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted.

This paper investigates a new approach to the quantification technique for road induced vehicle interior noise and vibration within the frequency range up to 40 Hz. By employing the least squares method, both vertical and fore-aft load to each wheel were quantified using transfer function and actual vibration response of the vehicle driven on a road. The coupled structural-acoustic vehicle model using the finite element method, which is also detailed in this paper, is combined with the quantified input load to simulate road induced interior noise and vibration response. Experimental verification, which indicates reasonable accuracy of the simulation, and an application for the prototype development are also presented.

The Unmanned Ground Vehicle Competition is jointly sponsored by the SAE, the Association for Unmanned Vehicle Systems (AUVS), and Oakland University. College teams, composed of both undergraduate and graduate students, build autonomous vehicles that compete by navigating a 139 meter outdoor obstacle course. The course, which includes a sand pit and a ramp, is defined by painted continuous or dashed boundary lines on grass and pavement. The obstacles are arbitrarily placed, multi-colored plastic-wrapped hay bales. The vehicles must be between 0.9 and 2.7 meters long and less than 1.5 meters wide. They must be either electric-motor or combustion-engine driven and must carry a 9 kilogram payload. All computational power, sensing and control equipment must be carried on board the vehicle. The technologies employed are applicable in Intelligent Transportation Systems (ITS).

The use of brakes on public roads depends on traffic conditions and the road itself. In order to research into brake usage on various roads and under various conditions, a micro-computer data logging system was developed. The recorded data were analyzed by a large computer and the severity of brake usages was defined. The relationship between the severity and the brake disc rotor temperature was derived, and used to predict brake pad wear/life. The predicted wear coincided fairly clossly with measured wear.

This paper describes a system which, in order to give a more natural and pleasing steering feeling, has incorporated vehicle speed and steering angle sensors in a hydraulic reaction type power steering system for control in accordance with the frequency of steering angles, control in accordance with vehicle speed and steering angle and control in accordance with forward and reverse acceleration. This system enables the steering characteristics to be matched to the road conditions; light steering for driving in the city, or increased responsiveness for winding mountain roads or sporty driving. The aims in developing this system and details of the method of control are described.

Attaining optimum balance between longitudinal compliance and sideforce compliance steer in a torsion beam suspension system is a challenging task. We developed a suspension in which the longitudinal compliance is almost doubled and the side force compliance steer amount is improved by using the link effect of toe control links. This suspension system has been developed to realize excellent controllability, stability, riding comfort, and road noise performance.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

In recent years Road Departure Mitigation Systems (RDMS) is introduced to the market for avoiding roadway departure collisions. To support the performance testing of the RDMS, the most commonly seen road edge, grass, is studied in this paper for the development of standard surrogate grass. This paper proposes a method for defining the resembling grass color and height features due to significant variations of grass appearances in different seasons, temperatures and environments. Randomly selected Google Street View images with grass road edges are gathered and analyzed. Image processing techniques are deployed to obtain the grass color distributions. The height of the grass is determined by referencing the gathered images with measured grass heights. The representative colors and heights of grass are derived as the specifications of surrogate grass for the standard evaluation of RDMS.