Search Results

Driver distraction is a topic of considerable interest, with the public debate centering on the use of cell phones and texting while driving. However, the driver distraction/overload issue is really much larger. It concerns specific tasks such as entering destinations on navigation systems, retrieving songs on MP3 players, accessing web pages, checking stocks, editing spreadsheets, and performing other tasks on smart phones, as well as, more generally, using in-vehicle information systems. Five major problems related to distraction/overload research and engineering and their solutions are addressed in this paper.

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.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Over half of the greenhouse gas (GHG) emissions in the United States come from the transportation and electricity generation sectors. To analyze the potential impact of cross-sector cooperation in reducing these emissions, we formulate a bi-level optimization model where the transportation sector can purchase renewable energy certificates (REC) from the electricity generation sector. These RECs are used to offset emissions from transportation in lieu of deploying high-cost fuel efficient technologies. The electricity generation sector creates RECs by producing additional energy from renewable sources. This additional renewable capacity is financed by the transportation sector and it does not impose additional cost on the electricity generation sector. Our results show that such a REC purchasing regime significantly reduces the cost to society of reducing GHG emissions. Additionally, our results indicate that a REC purchasing policy can create electricity beyond actual demand.

A new generation of a planetary-gear-based automatic transmission system is designed with an increasing number of ratio steps. It requires synchronous operation of one or more wet clutches, to achieve a complex shift event. A missed synchronization results in drive torque disturbance which may be perceived by vehicle occupants as an undesirable shift shock. Accurate knowledge of clutch behaviors in an actual vehicle environment is indispensable for achieving precise clutch controls and reducing shift calibration effort. Wet clutches are routinely evaluated on an industry-standard SAE#2 tester during the clutch design process. While it is a valuable tool for screening relative frictional behaviors, clutch engagement data from a SAE#2 tester do not correlate well with vehicle shift behaviors due to the limited reproducibility of realistic slip, actuator force profiles, and lubrication conditions.

Ball nut assemblies (BNAs) are used in a variety of applications, e.g., automotive, aerospace and manufacturing, for converting rotary motion to linear motion (or vice versa). In these application areas, accurate characterization of the dynamics of BNAs using low-order models is very useful for performance simulation and analyses. Existing low-order contact load models of BNAs are inadequate, partly because they only consider the axial deformations of the screw and nut. This paper presents a low-order load distribution model for BNAs which considers the axial, torsional and lateral deformations of the screw and nut. The screw and nut are modeled as finite element beams, while Hertzian Contact Theory is used to model the contact condition between the balls and raceways of the screw and nut. The interactions between the forces and displacements of the screw and nut and those at the ball-raceway contact points are established using transformation matrices.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

Half of the car occupant deaths involved in two-vehicle crashes results from side impact collisions. In an attempt to better understand the role that vehicle mass plays in crashes and injury causation, detailed information from the NASS CDS database on injury source was distributed in three classes: contact with intrusion, contact without intrusion, and restrained acceleration or non-contact. We compared these distributions for belted drivers in side verses frontal crashes. When looking at the type of striking, or bullet, vehicle in near-side impacts, we found that intrusion injuries are more prevalent in cars hit by SUVs and pickups than by other cars. We also looked at the body region injured verses the type of striking vehicle and found head injuries to be slightly more prevalent when the striking vehicle is an SUV or pick-up. Data from the University of Michigan CIREN case studies on side impacts are presented and are consistent with the NASS CDS data.

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.

Thermal distortions of friction disks caused by frictional heating modify pressure distribution on friction surfaces. Pressure distribution, in turn, determines distribution of generated frictional heat. These interdependencies create a complex thermoelastic system that, under some conditions, may become unstable and may lead to severe pressure concentrations with very high local temperature and stress. The phenomenon is responsible for many common thermal failure modes of friction elements and is known as frictionally excited thermoelastic instability (TEI). In the paper, one of the cases of TEI is investigated theoretically and experimentally. The study involves a two-disk structure with one fiction disk and one matching steel disk that have one friction interface. An unsteady heat conduction problem and an elastic contact problem are modeled as axisymmetric ones and are solved using the finite element method.

While the results are generally the most exciting aspects of an LCI study, the details of the LCI model that generates the results are equally significant; particularly when modeling the life cycle of an automobile. The modeling challenges faced in conducting the US AMP LCI of a mid-sized vehicle based on the 1995 Lumina, Intrepid and Taurus are highlighted. The number of parts (over 20,000), supply chain complexity, materials composition, and the demanding set of OEM requirements for model features required special LCI methods and solutions. The LCI model and selected results are compared with previous studies, and recommendations for improvements in the USAMP LCI model are also provided. This paper is one of six SAE publications discussing the results and execution of the USCAR AMP Generic Vehicle LCI. The papers in this series are (Overview of results 982160, 982161, 982162, 982168, 982169, 982170).

The chance of vehicle collisions significantly increases when drivers are overloaded with information from in-vehicle systems. Developing adaptive workload management systems (AWMS) to dynamically control the rate of messages from these in-vehicle systems is one of the solutions to this problem. However, existing AWMSs do not use a model of driver cognitive system to estimate workload and only suppress or redirect in-vehicle system messages, without changing their rate based on driver workload. In this work, we propose a prototype of a new adaptive workload management system (QN-MHP AWMS) and it includes: a queueing network model of driver workload (Wu & Liu, In Press) that estimates driver workload in different driving situations, and a message controller that determines the optimal delay times between messages and dynamically controls the rate of messages presented to drivers.

A Magic Cube (MQ) approach for crashworthiness design has been proposed in previous research [1]. The purpose of this paper is to extend the MQ approach to the blast protection design of a military vehicle system. By applying the Space Decompositions and Target Cascading processes of the MQ approach, three subsystem design problems are identified to systematize the blast protection design problem of a military vehicle. These three subsystems, including seat structure, restraint system, and under-body armor structure, are most influential to the overall blast-protective design target. The effects of a driver seat subsystem design and restraint-system subsystem design on system blast protection are investigated, along with a focused study on the under-body blast-protective structure design problem.

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.

Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

NASS 80-88 passenger side impacts data were analyzed. Location of primary car damage using the CDC classification, the AIS for injury severity studies, and the interior contacts of the various body areas. Drivers alone, or with passengers were studied separately in both left and right side crashes. Direct impacts to the passenger compartment only are less frequent than to other CDC side zones. Driver interior contacts vary by body region but also by side impacted in the crash. The presence of an unrestrained front passenger appears to enhance driver injury level in left side crashes but the presence of a passenger, in right side crashes appears to moderate driver injury severity.

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

With increasingly stringent light duty particulate emissions regulations, it is of great interest to better understand particulate matter formation. Helping to build the knowledge base for a thorough understanding of particulate matter formation will be an essential step in developing effective control strategies. It is especially important to do this in such a way as to emulate real driving behaviors, including cold starts and transients. To this end, this study examined particulate emissions during transient operation in a recent model year vehicle equipped with a GDI engine. Three of the major federal test cycles were selected as evaluation schemes: the FTP, the HWFET, and the US06. These cycles capture much of the driving behaviors likely to be observed in typical driving scenarios. Measurements included particle size distributions from a TSI EEPS fast-response particle spectrometer, as well as real-time soot emissions from an AVL MSS soot sensor.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

Automated clutches for vehicle startup is being increasingly deployed in commercial trucks for benefits, which include driver comfort, gradient performance, improved clutch life, emissions and driveline vibration reduction potential. The process of designing the controller is divided into 2 parts. Firstly, the parameter estimation of previously developed driveline models is carried out. The procedure involves an off-line minimization technique based on measured and estimated speeds. Secondly, the nominal plant model is used to develop LQR based optimal control strategy, which takes into account the slip time, dissipated power and slip acceleration. Mathematical expression of the performance index is clearly developed. A variety of clutch lock up profiles can be incorporated by changing a single tuning parameter, thus providing the driver the ability to select a launch profile based on specific driving objectives.