Search Results

Transient road disturbances excite complex vehicle responses involving the interaction of suspension/chassis, powertrain, and body systems. Typical ones are due to the interactions between tires and road expansion joints, railway crossings and other road discontinuities. Such transient disturbances are generally perceived as “impact harshness” due to the harshness perception as sensed by drivers through both sound and vibration. This paper presents a study of quantifying the effects of sound, steering wheel and seat/floorpan vibrations on the overall perception of the “impact harshness” during impact transient events. The Vehicle Vibration Simulator (VVS) of the Ford Research Laboratory was used to conduct this study. The results of the study show that sound and vibration have approximately equal impact on the overall perception of impact harshness. There is no evidence of interaction between sound and vibration.

A wavelet-based technique for reducing the impulsive character of sound recordings is presented. The amount of impulsive content removed may be adjusted by varying a statistical threshold. The technique is validated for a diesel idle sound-quality application. The wavelet-based modification produces a substantial decrease in impulsive character as verified by an objective sound-quality metric for engine “ticking”. Informal subjective assessment of the modified results found them to be realistic and free from artifacts. The procedure is expected to be useful for sound-quality simulation and target-setting for diesel powertrain noise and other automotive sounds containing both impulsive and non-impulsive content.

An age-dependent, serious-to-fatal (AIS3+), thoracic risk curve was derived and evaluated for frontal impacts. The study consisted of four parts. In Part 1, two datasets of post mortem human subjects (PMHS) were generated for statistical and sensitivity analyses. In Part 2, logistic regression analyses were conducted. For each dataset, two statistical methods were applied: (1) a conventional maximum likelihood method, and (2) a modified maximum likelihood method. Therefore, four statistical models were derived — one for each dataset/statistical method combination. For all of the resulting statistical models (risk curves), the linear combination of maximum normalized sternum deflection and age of the PMHS was identified as a feasible predictor of AIS3+ thoracic injury probability. In Part 3, the PMHS-based risk curves were transformed into test-dummy-based risk curves. In Part 4, validation studies were conducted for each risk curve.

This paper describes the design and build of an experimental super transport truck for high-speed, long distance freight hauling on the interstate highway system of the 1970's. The tractor, powered by a 600-hp gas turbine engine, pulls two 40-foot tandem axle trailers at a G.C.W. of 170,000 lbs. Details of the turbine engine development are covered in SAE paper, No. 991B. One of the features of the super transport truck is the cab, which is designed for long-distance, non-stop, two-man operation. It is provided with sleeping accommodations, washroom conveniences, food facilities, and a complete heating and air-conditioning system. The 13-foot high cab roof is flush with the top of the trailers, providing a substantial aerodynamic advantage. Other features and components of the truck are described, and observations made during the 5500-mile national tour are discussed.

Utilization of statistical methods can improve vehicle stopping-distance projections and reduce the complexity of brake deceleration models. These techniques can be very useful in the course of ascertaining whether an untested vehicle conforms to the applicable Federal Motor Vehicle Safety Standard (FMVSS), but they have much broader uses in the design of brake systems.

The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent.

People and their communities are looking for transportation solutions that reduce travel time, improve well-being and accessibility, and reduce emissions and traffic congestion. Although new mobility services like ride-hailing advertise improvements in these areas, closer inspection has revealed a discrepancy between industry claims and reality. Key decision-makers, including citizens, cities and enterprise, and mobility service providers have the opportunity to leverage connected vehicle and connected device data through cloud-based APIs. We propose a GHG data analytics framework that functions on top of a cloud platform to provide unique system-level perspectives on operating transportation services, from procuring the most environmentally and people friendly vehicles to scheduling and designing the services based on data insights.

Statistical energy analysis (SEA) has recently emerged as an effective tool for design assessment in the automotive industry. Automotive OEM companies develop vehicle models to aid design of body and chassis systems. The tire and wheel suppliers develop and supply component models to OEM companies in the engineering stage. In the model development process, some information on the vehicle side or component side is necessary for model development and correlation. A suitable termination representation of the vehicle characteristics on the tire/wheel model is required. This termination should account for the dissipation of energy on vehicle body and chassis side, otherwise the component model will overestimate the vibration responses and energy levels. On the vehicle model side, a representative simplified tire/wheel model may be sufficient for full vehicle road noise simulation.

Do older light trucks, often with second (and subsequent) owners, present a higher risk to either their own occupants or to other road users? And is the safety record for newer trucks better or worse than the record for their older counterparts? To answer these questions, fatalities in crashes involving at least one light truck were examined using the Fatal Analysis Reporting System (FARS). Fatality rates for both occupants of the light truck and for other road users (occupants of other motor vehicles, pedestrians, etc.) in these crashes were computed, based both on the number of registered vehicles and on the vehicle miles of travel. Two trends in these fatality rates are observed. First, as light trucks age, a consistent decline is found in risk both to their own occupants and to other road users. Second, a distinct decrease is found in road user risk for newer light trucks compared to older light trucks when they were new, both for their own occupants and for other road users.

Camera data generated in a 3D virtual environment has been used to train object detection and identification algorithms. 40 common US road traffic signs were used as the objects of interest during the investigation of these methods. Traffic signs were placed randomly alongside the road in front of a camera in a virtual driving environment, after the camera itself was randomly placed along the road at an appropriate height for a camera located on a vehicle’s rear view mirror. In order to best represent the real world, effects such as shadows, occlusions, washout/fade, skew, rotations, reflections, fog, rain, snow and varied illumination were randomly included in the generated data. Images were generated at a rate of approximately one thousand per minute, and the image data was automatically annotated with the true location of each sign within each image, to facilitate supervised learning as well as testing of the trained algorithms.

With the increasing number of cars on the street, the exchange of information between those cars becomes essential to improve the driving skills of each driver, resulting in a safer, intelligent and more dynamic traffic. The task now is to make it accessible for everyone. One possible and cheap way to solve this issue is to seek possibilities on free technologies within market trends. Using the smartphone platforms, which holds a high level of embedded technologies, becoming a global communication device even to interpersonal and to social networks, and AppLink Development Kit for smartphones and vehicles integration, this paper will cover aspects about the integration of the kit to an database application based on the cloud, enabling real-time interaction between two cars. Making possible to a driver have access to information and current status of other cars to aid ones life on heavy traffic.

The interaction of Connect and Automated vehicles (CAV) with regular vehicles in the traffic stream has been extensively researched. Most studies, however, focus on calibrating driver behavior models for CAVs based on various levels of automation and driver aggressiveness. Other related studies largely focus on the coordination of CAVs and infrastructure like traffic signals to optimize traffic. However, the effects of different strategic flow management of CAVs in the traffic stream in the comparative scenario-based analysis is understudied. Thus, this study develops a framework and simulations for integrating CAVs in a corridor section. We developed a calibrated model with CAVs for a corridor section in Corktown, Michigan, and simulate how dedicated CAV lane operations can be implemented without significant change in existing infrastructure.

This paper presents a study performed in 10 vehicles available in Brazilian market where the drivability with ethanol and gasoline, also referred as gasohol were compared. The motivation for this work came from the constant competition of the automotive industry, where engineers are searching for ways to improve the quality of the products aiming the “best in class” drivability with the best cost efficiency. For the Brazilian market, a further complexity is added to the development and verification process, which is the need to design and verify the controls and calibration considering the two fuels available in the market, the ethanol and the gasoline. In order to determine how the drivability is impacted by the ethanol, the paper presents a study where the drivability data were generated using the objective drivability measurement system AVL-DRIVE™.

Driver injury probabilities in real-world frontal crashes were statistically modeled to estimate the relative roles of five variables of topical interest. One variable pertained to behavior (belt-wearing rate), one pertained to crash circumstances (speed change), and three pertained to occupant demographics (sex, age, and body mass index). The attendant analysis was composed of two parts: (1) baseline statistical modeling to help recover the past, and (2) sensitivity analyses to help consider the future. In Part 1, risk functions were generated from statistical analysis of real-world data pertaining to 1998-2014 model-year light passenger cars/trucks in 11-1 o’clock, full-engagement frontal crashes documented in the National Automotive Sampling System (NASS, 1997-2014). The selected data yielded a weighted estimate of 1,269,178 crash-involved drivers.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

The use of statistics is an important aspect in the analysis of fatigue test results. This paper describes the use of the Maximum Likelihood Estimate to analyze the fatigue results of staircase tests and Life-Regression Models (S-N curve). Two life-regression models are analyzed: a modified Basquin model and the Random Fatigue Limit (RFL) model.

Gasoline particulate filters (GPF) are used as an efficient solution to reduce particulate matter (PM) emissions on gasoline vehicles. GPFs are ceramic wall-flow filters and are normally located downstream of conventional three-way catalysts (TWC) [1]. The study in this paper is intended to evaluate the impact of drive cycle and ambient temperature on modelled GPF soot accumulation and regeneration. The test data were obtained through real road testing in Chinese cities including Nanjing, Hainan and Harbin. Five 2.0 L gasoline turbo direct-injection (GTDI) prototype vehicles from several China Stage 6 applications were employed for the road tests. The results of the testing indicated that a drive cycle with low engine speed and engine load, like a typical city road in rush hour traffic in Nanjing, had a low probability of generating high GPF temperatures (> 600 °C) and sufficient oxygen to regenerate the GPF.

Lower-body injury data for adults in real-world frontal impacts in the National Automotive Sampling System (NASS) were collected, analyzed, and modeled via statistical methods. Two levels of lower-body injury were considered: maximum serious-to-fatal (MAIS3+) and moderate-to-fatal (MAIS2+). In the analysis, we observed that a substantial fraction of all lower-body injured occupants had no recorded floor/toe pan intrusion: 47% of all MAIS3+ injured occupants; 69% of all MAIS2+ injured occupants. In the statistical modeling, we developed binary logistic regression models to fit the MAIS3+ and MAIS 2+ injury data. The statistically significant variables (p ≤ 0.05) were the speed change of the crash, postcrash floor/toe pan intrusion, level of restraint, occupant age, and occupant gender.

The National Highway Traffic Safety Administration (NHTSA) recently published a Request for Comments regarding a potential upgrade to the US New Car Assessment Program (US NCAP) - a star-rating program pertaining to vehicle crashworthiness. Therein, NHTSA (a) cited two metrics for assessing head risk: Head Injury Criterion (HIC15) and Brain Injury Criterion (BrIC), and (b) proposed to conduct risk assessment via its risk curves for those metrics, but did not prescribe a specific method for applying them. Recent studies, however, have indicated that the NHTSA risk curves for BrIC significantly overstate field-based head injury rates. Therefore, in the present three-part study, a new set of BrIC-based risk curves was derived, an overarching head risk equation involving risk curves for both BrIC and HIC15 was assessed, and some additional candidate-predictor-variable assessments were conducted. Part 1 pertained to the derivation.