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Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement. Click here to access the full SAE EDGETM Research Report portfolio.

An optimal energy management strategy (Optimal EMS) can yield significant fuel economy (FE) improvements without vehicle velocity modifications. Thus it has been the subject of numerous research studies spanning decades. One of the most challenging aspects of an Optimal EMS is that FE gains are typically directly related to high fidelity predictions of future vehicle operation. In this research, a comprehensive dataset is exploited which includes internal data (CAN bus) and external data (radar information and V2V) gathered over numerous instances of two highway drive cycles and one urban/highway mixed drive cycle. This dataset is used to derive a prediction model for vehicle velocity for the next 10 seconds, which is a range which has a significant FE improvement potential. This achieved 10 second vehicle velocity prediction is then compared to perfect full drive cycle prediction, perfect 10 second prediction.

This study was designed to investigate how the spectral power distribution (SPD) of LED headlamps (including correlated color temperature, CCT) affects both objective driving performance and subjective responses of drivers. The results of this study are not intended to be the only considerations used in choosing SPD, but rather to be used along with results on how SPD affects other considerations, including visibility and glare. Twenty-five subjects each drove 5 different headlamps on each of 5 experimental vehicles. Subjects included both males and females, in older (64 to 85) and younger (20 to 32) groups. The 5 headlamps included current tungsten-halogen (TH) and high-intensity discharge (HID) lamps, along with three experimental LED lamps, with CCTs of approximately 4500, 5500, and 6500 K. Driving was done at night on public roads, over a 21.5-km route that was selected to include a variety of road types.

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.

The attractiveness of the hydraulic hybrid concept stems from the high power density and efficiency of the pump/motors and the accumulator. This is particularly advantageous in applications to heavy vehicles, as high mass translates into high rates of energy flows through the system. Using dry case hydraulic pumps further improves the energy conversion in the system, as they have 1-4% better efficiency than traditional wet-case pumps. However, evacuation of fluid from the case introduces air bubbles and it becomes imperative to address the deaeration problems. This research develops a bubble elimination efficiency testing apparatus (BEETA) to establish quantitative results characterizing bubble removal from hydraulic fluid in a cyclone deaeration device. The BEETA system mixes the oil and air according to predetermined ratio, passes the mixture through a cyclone deaeration device, and then measures the concentration of air in the exiting fluid.

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.

Many highway accidents are caused by distracted drivers and those suffering from drowsy driver syndrome. A driver alert indicating a lane departure could thwart such accidents, saving lives and making our roads safer. Products called Lane Departure Warning Systems (LDWS) have been developed to alert drivers of a lane departure. However, due to their high cost, lane departure warning systems are available only on luxury vehicles, barring their benefits from the majority of drivers. With Field Programmable Gate Arrays (FPGA) becoming more powerful and more affordable, a LDWS implementation utilizing hardware rather than software to conduct image processing eliminates the need for a costly high-power microprocessor, and could bring LDWS to a broader user base. This paper will discuss an FPGA based approach to LDWS. The proof-of-concept system is based on a Xilinx FPGA, taking its image data from an off-the-shelf NTSC camera.

The Moulton Hydragas suspension system improves small car ride quality by interconnecting the front and rear wheel on each side of the vehicle via a hydraulic fluid pipe between the front and rear dampers. A Hydragas system from a Rover Group MGF sports car was statically and dynamically tested to generate stiffness and damping coefficient matrices. The goal was to develop the simplest possible model of the system for use in ride quality studies. A linear model showed reasonable accuracy over restricted frequency ranges. A second model used bilinear spring and damping constants, and was more accurate for predicting force at both the front and rear units for frequencies from 1 to 8 Hz. The Hydragas system static stiffness parameters, when used in the model, caused peak force underprediction in the jounce direction. The bilinear model required increased jounce stiffness to account for hysteresis in the rubber elements of the system, and dynamic fluid flow phenomena.

Testing for vehicle emissions and fuel economy certification occurs primarily on chassis dynamometers in a laboratory setting and therefore the actual road conditions, such as forces due to tire rolling resistance and internal friction, must be simulated. Test track coastdown procedures measure vehicle road load forces and produce an equation which relates these forces to velocity. The recent inclusion of onboard anemometry has allowed the coastdown procedure to account for varying wind effects; however, the new anemometer based mechanical loss coefficients do not take into account ambient weather conditions. The two purposes of this study are (1) to determine the new tire rolling resistance temperature correction coefficient that should be used when test ambient temperature is different from the standard reference value of 68°F, and (2) to investigate the effects of auxiliary measurements, such as other ambient conditions and vehicle settings, on this correction coefficient.

This paper presents the theory and experimental performance of a system for detecting engine misfires in automobiles. The method is potentially suitable for meeting the California Air Resources Board (CARB) requirements under On Board Diagnostics II (OBDII) rules. The instrumentation for the present method measures (noncontacting) crankshaft instantaneous angular speed. Highly efficient signal processing algorithms permit detection of each individual misfire. The performance of the present method is expressed in terms of error rates made in detecting individual misfires. Normal operating conditions yield error rates under 10-4. Under worst case conditions consisting of light load, high RPM and rough roads with the torque converter in lockup are under 10-3.

Intelligent Vehicle-Highway Systems (IVHS) improve the operation of cars and trucks on public roads by combining information technology with road transportation technologies. The basic ideas about IVHS are by no means new but a number of converging forces have encouraged significant IVHS development in North America recently. Based on the results of a Delphi survey to project realistic future scenarios, both applied and fundamental research agenda are being formulated in a Michigan-based IVHS program to push the IVHS technologies for advanced motorist information systems and for backup vehicle controls under emergency conditions. The scope of the research agenda includes social/human elements as well as hardware and software technological systems. The Michigan research program is expected to contribute to the development of IVHS in North America, both technically and institutionally.

Strain gage instrumented transducers were used to measure the cord loads at a number of locations in several different automotive tires loaded against both flat and cylindrical road wheel surfaces. The two basic types of cord load fluctuation encountered in all automobile tires have been identified from these measurements, and the most severe location for cord load fluctuations has been closely bracketed. By these measurements, it has been possible to show that for each tire definite relations exist between the cord loads induced while running on a cylindrical drum and while running on a flat surface. The maximum cord load fluctuations in a tire are the same for the NBS roadwheel and flat surface when the tire is loaded against the roadwheel with a load of between 85 and 90% of that used on the flat surface.