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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?”

The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature].

Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality.

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.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

An experimental investigation of non-intrusive combustion sensing was performed using a tri-axial accelerometer mounted to the engine block of a small-bore high-speed 4-cylinder compression-ignition direct-injection (CIDI) engine. This study investigates potential techniques to extract combustion features from accelerometer signals to be used for cycle-to-cycle engine control. Selection of accelerometer location and vibration axis were performed by analyzing vibration signals for three different locations along the block for all three of the accelerometer axes. A magnitude squared coherence (MSC) statistical analysis was used to select the best location and axis. Based on previous work from the literature, the vibration signal filtering was optimized, and the filtered vibration signals were analyzed. It was found that the vibration signals correlate well with the second derivative of pressure during the initial stages of combustion.

Growing environmental concerns and stringent vehicle emissions regulations has created an urge in the automotive industry to move towards electrified propulsion systems. Reducing and eliminating the emission from public transportation vehicles plays a major role in contributing towards lowering the emission level. Battery electric buses are regarded as a type of promising green mass transportation as they provide the advantage of less greenhouse gas emissions per passenger. However, the electric bus faces a problem of limited range and is not able to drive throughout the day without being recharged. This research studies a public bus transit system example which servicing the city of Ann Arbor in Michigan and investigates the impact of different electrification levels on the final CO2 reduction. Utilizing models of a conventional diesel, hybrid electric, and battery electric bus, the CO2 emission for each type of transportation bus is estimated.

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.

Warranty forecasting of repairable systems is very important for manufacturers of mass produced systems. It is desired to predict the Expected Number of Failures (ENF) after a censoring time using collected failure data before the censoring time. Moreover, systems may be produced with a defective component resulting in extensive warranty costs even after the defective component is detected and replaced with a new design. In this paper, we present a forecasting method to predict the ENF of a repairable system using observed data which is used to calibrate a Generalized Renewal Processes (GRP) model. Manufacturing of products may exhibit different production patterns with different failure statistics through time. For example, vehicles produced in different months may have different failure intensities because of supply chain differences or different skills of production workers, for example.

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.

Exhaust Gas Recirculation (EGR) coolers are commonly used in diesel and modern gasoline engines to reduce the re-circulated gas temperature. A common problem with the EGR cooler is a reduction of the effectiveness due to the fouling layer primarily caused by thermophoresis, diffusion, and hydrocarbon condensation. Typically, effectiveness decreases rapidly at first, and asymptotically stabilizes over time. There are several hypotheses of this stabilizing phenomenon; one of the possible theories is a deposit removal mechanism. Verifying such a mechanism and finding out the correlation between the removal and stabilization tendency would be a key factor to understand and overcome the problem. Some authors have proposed that the removal is a possible influential factor, while other authors suggest that removal is not a significant factor under realistic conditions.

We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

Lean NOx Traps (LNTs) are one type of lean NOx reduction technology typically used in smaller diesel passenger cars where urea-based Selective Catalytic Reduction (SCR) systems may be difficult to package . However, the performance of lean NOx traps (LNT) at temperatures above 400 C needs to be improved. The use of Rapidly Pulsed Reductants (RPR) is a process in which hydrocarbons are injected in rapid pulses ahead of a LNT in order to expand its operating window to higher temperatures and space velocities. This approach has also been called Di-Air (diesel NOx aftertreatment by adsorbed intermediate reductants) by Toyota. There is a vast parameter space which could be explored to maximize RPR performance and reduce the fuel penalty associated with injecting hydrocarbons. In this study, the mixing uniformity of the injected pulses, the type of reductant, and the concentration of pulsed reductant in the main flow were investigated.

Automotive seats are commonly described by one-dimensional measurements, including those documented in SAE J2732. However, 1-D measurements provide minimal information on seat shape. The goal of this work was to develop a statistical framework to analyze and model the surface shapes of seats by using techniques similar to those that have been used for modeling human body shapes. The 3-D contour of twelve driver seats of a pickup truck and sedans were scanned and aligned, and 408 landmarks were identified using a semi-automatic process. A template mesh of 18,306 vertices was morphed to match the scan at the landmark positions, and the remaining nodes were automatically adjusted to match the scanned surface. A principal component (PC) analysis was performed on the resulting homologous meshes. Each seat was uniquely represented by a set of PC scores; 10 PC scores explained 95% of the total variance. This new shape description has many applications.

Many Intelligent Transportation System (ITS) technologies have been developed to improve the safety and efficiency of cars, trucks, public transport and infrastructure. However, very few ITS have been developed specifically for the motorcycle user protection. In this paper an analysis of dynamic and static communications tests between a vehicle and two motorcycles are provided. The system enables vehicles and motorcycles to exchange safety information such as speed, heading, location, and brake status through the use of 5.9 GHz Dedicated Short Range Communication (DSRC) protocol. The vehicles and motorcycles can then assess the potential threat level based on the incoming messages from the nearby traffic. Several high-impact motorcycle-to-vehicle collision scenarios are analyzed. Technical challenges, such as motorcycle wireless unit antenna direction performance, communication performance and target classification accuracy are further investigated.

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 purpose of this paper is to demonstrate the impact of low- floor bus seating configuration, passenger load factor (PLF) and passenger characteristics on individual boarding and disembarking (B-D) times -a key component of vehicle dwell time and overall transit system performance. A laboratory study was conducted using a static full-scale mock-up of a low-floor bus. Users of wheeled mobility devices (n=48) and walking aids (n=22), and visually impaired (n=17) and able-bodied (n=17) users evaluated three bus layout configurations at two PLF levels yielding information on B-D performance. Statistical regression models of B-D times helped quantify relative contributions of layout, PLF, and user characteristics viz., impairment type, power grip strength, and speed of ambulation or wheelchair propulsion. Wheeled mobility device users, and individuals with lower grip strength and slower speed were impacted greater by vehicle design resulting in increased dwell time.

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.

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.