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A method for detecting drivers' intentions is essential to facilitate operating mode transitions between driver and driver assistance systems. We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. HMM-based steering behavior models for emergency and normal lane changes as well as for lane keeping were developed using a moving base driving simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes is possible using HMMs.

The challenge of developing a robust, real-time driver gaze classification system is that it has to handle difficult edge cases that arise in real-world driving conditions: extreme lighting variations, eyeglass reflections, sunglasses and other occlusions. We propose a single-camera end-toend framework for classifying driver gaze into a discrete set of regions. This framework includes data collection, semi-automated annotation, offline classifier training, and an online real-time image processing pipeline that classifies the gaze region of the driver. We evaluate an implementation of each component on various subsets of a large onroad dataset. The key insight of our work is that robust driver gaze classification in real-world conditions is best approached by leveraging the power of supervised learning to generalize over the edge cases present in large annotated on-road datasets.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

Cranking and startup fuel control has become increasingly important due to ever tightening emission requirements. Additionally, engine-off strategies during idle will require substantially more engine startup events with the associated need for very clean starts. Thus, knowledge of an engine's Air-Fuel Ratio (AFR) during its early cycles is necessary in order to optimize cranking and startup fueling. This paper examines and compares two methods of measuring an engine's AFR during engine startup (approximately the first second of operation); an in-cylinder technique using a Fast Flame Ionization Detector (FFID) and the conventional exhaust based Universal Exhaust Gas Oxygen (UEGO) sensor method. Engine starts using a Ford Zetec engine were performed at three different temperatures (0, 20 and 90 C) as well as different initial engine starting positions.

This paper describes a post-race analysis of team KOMMIT EVT’s electric motorcycle data collected during the 2016 Pikes Peak International Hill Climb (PPIHC). The motorcycle consumed approximately 4 kWh of battery energy with an average and maximum speed of 107 km/h and 149 km/h, respectively. It was the second fastest electric motorcycle with a finishing time of 11:10.480. Data was logged of the motorcycle’s speed, acceleration, motor speed, power, currents, voltages, temperatures, throttle position, GPS position, rider’s heart rate and the ambient environment (air temperature, pressure and humidity). The data was used to understand the following factors that may have prevented a faster time: physical fitness of the rider, thermal limits of the motor and controller, available battery energy and the sprocket ratio between the motor and rear wheel.

The aim of this study was to explore if fingernail delamination injury following EMU glove use may be caused by compression-induced blood flow occlusion in the finger. During compression tests, finger blood flow decreased more than 60%, however this occurred more rapidly for finger pad compression (4 N) than for fingertips (10 N). A pressure bulb compression test resulted in 50% and 45% decreased blood flow at 100 mmHg and 200 mmHg, respectively. These results indicate that the finger pad pressure required to articulate stiff gloves is more likely to contribute to injury than the fingertip pressure associated with tight fitting gloves.

To address the growing need for detailed chemistry in engine simulations, new software tools and validated data sets are being developed under an industry-funded consortium involving members from the automotive and fuels industry. The results described here include systematic comparison and validation of detailed chemistry models using a wide range of fundamental experimental data, and the development of software tools that support the use of detailed mechanisms in engineering simulations. Such tools include the automated reduction of reaction mechanisms for targeted simulation conditions. Selected results are presented and discussed.

Human explorers of planetary surfaces would benefit greatly from a spacesuit design that facilitates locomotion. To aid in the development of such an extravehicular activity suit, a design effort incorporating the concept of mechanical counter pressure (MCP) was undertaken. Three-dimensional laser scanning of the human body was used to identify the main effects of knee flexion angle on the size and shape of the leg. This laser scanning quantified the changes in shape that must be supported by an MCP garment and the tension that must be developed to produce even MCP. Evaluation of a hybrid-MCP concept using inextensible materials demonstrated strong agreement between experimental data and a mathematical model with rigid cylinder geometry. Testing of a form-fitting garment on the right lower leg of a subject demonstrated successful pressure production. Further research is required to evaluate how evenly pressure can be distributed using the hybrid-MCP concept.

Knowledge mapping is a first and mandatory step in creation of system architecture. This paper considers the conceptual modeling of automotive systems, and discusses the creation of a knowledge-based model with respect to the Object Process Methodology an approach used in designing intelligent systems by depicting them using object models and process models. With this knowledge, systems engineer should consider what a product is comprised of (its structure), how it operates (its dynamics), and how it interacts with the environment. As systems have become more complex, a prevalent problem in systems development has been the number of accruing errors. A clearly defined and consistent mapping of knowledge regarding structure, operation and interaction is necessary to construct an effective and useful system. An interactive, iterative and consistent method is needed to cope with this complex and circular problem.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.

The fuel energy consumption of subsonic air transportation is examined. The focus is on identification and quantification of fundamental engineering design tradeoffs which drive the design of subsonic tube and wing transport aircraft. The sensitivities of energy efficiency to recent and forecast technology developments are also examined.

Draw beads are widely utilized as a mechanism for providing proper restraining force to a sheet in a forming operation. In this paper, numerical simulations using the nonlinear finite element method are conducted to model the process of drawing a sheet through various draw bead configurations to study the mechanics of draw bead restraint. By examing the sensitivity of the draw bead restraining force due to the change of the draw bead penetration, the work shows that the penetration has the potential to be a very good element for varying and controlling restraining force during the process. A closed-loop feedback control of draw bead penetration using a proportional-integral controller is achieved by the combination of the original finite element simulation and a special element which links penetration to a pre-defined restraining force trajectory.

Lubricating oil consumption (LOC) is a direct source of hydrocarbon and particulate emissions from internal combustion engines. LOC also inhibits the lifetime of exhaust aftertreatment system components, preventing their ability to effectively filter out other harmful emissions. Due to its influence on piston ring- bore conformability, bore distortion is arguably the most critical parameter for engine designers to consider in prevention of LOC. Bore distortion also has a significant influence on the contact forces between the piston ring and cylinder wall, which determine the wear rate of the ring and cylinder wall and can cause durability issues. Two drivers of bore distortion: thermal expansion and head bolt stresses, are routinely considered in conformability and contact analyses. Separately, bore distortion/vibration due to piston impact and combustion/cylinder pressures has been previously analyzed in wet liner engines for coolant cavitation and noise considerations.

One of the goals of designing engineering systems is to maximize the system's reliability. A reliable system must satisfy its functional requirements without failure throughout its intended lifecycle. The typical means to achieve a desirable level of reliability is through preventive maintenance of a system; however, this involves cost. A more fundamental approach to the problem is to maximize the system's reliability by preventing failures from occurring. A key question is to find mechanisms (and the means to implement them into a system) that will prevent its system range from going out of the design range. Functional periodicity is a means to achieve this goal. Three examples are discussed to illustrate the concept. In the new electrical connector design, it is the geometric functional periodicity provided by the woven wire structure. In the case of integrated manufacturing systems, it is the periodicity in scheduling of the robot motion.

Extravehicular activity (EVA) is investigated through experiments testing an actual extravehicular mobility unit (EMU) performing several EVA tasks in the laboratory, and a dynamic model of the EMU space suit is developed. Building directly on earlier work in EVA simulation, the space suit model was created from mass, inertia, and performance data to augment the unsuited 12-segment human model used in previous studies. A modified Preisach model was used to mathematically describe the hysteretic torque characteristics of joints in a pressurized space suit, and implemented numerically based on observed suit parameters. Computational simulations, based loosely on a 1995 EVA involving manipulation of the Spartan astrophysics payload, were performed to observe the effect of suit constraints on simulated astronaut performance.

Three-piece oil control rings (TPOCR) are widely used in the majority of modern gasoline engines and they are critical for lubricant regulation and friction reduction. Despite their omnipresence, the TPOCRs’ motion and sealing mechanisms are not well studied. With stricter emission standards, gasoline engines are required to maintain lower oil consumption limits, since particulate emissions are strongly correlated with lubricant oil emissions. This piqued our interest in building a numerical model coupling TPOCR dynamics and oil transport to explain the physical mechanisms. In this work, a 2D dynamics model of all three pieces of the ring is built as the main frame. Oil transport in different zones are coupled into the dynamics model. Specifically, two mass-conserved fluid sub-models predict the oil movement between rail liner interface and rail groove clearance to capture the potential oil leakage through TPOCR. The model is applied on a 2D laser induced fluorescence (2D-LIF) engine.

The nearly 77 million baby boomers, born between 1946 and 1964, can say that they are the automobile generation. Now turning 60 one every seven seconds, what are the new safety challenges and opportunities posed by the next generation of older adults? This paper presents a modified Haddon matrix to identify key product development, design and liability issues confronting the automobile industry and related stakeholders. The industry is now at a critical juncture to address the development of key technological innovations as well as the changing policy and liability environments being reshaped by an aging population.

With the increasing complexity of automotive systems and the related increasing use of software in them, new approaches are needed to ensure safety. In these new types of automotive systems, safety and reliability are different and require different engineering approaches. Accidents are increasingly due to design errors and to dysfunctional interactions among components rather than component failure. In addition, safety must be engineered and built into the design from the beginning; it is not possible to effectively and affordably add safety devices onto a finished design. This paper describes the need for new approaches to automotive safety and describes an alternative to the traditional reliability-based approaches to safety engineering. The new approach is based on systems theory and views accidents in terms of lack of control or enforcement of the behavioral constraints required to ensure safety.

Advanced driver assistance systems (ADAS) are an increasingly common feature of modern vehicles. The influence of such systems on driver behavior, particularly in regards to the effects of intermittent warning systems, is sparsely studied to date. This paper examines dynamic changes in physiological and operational behavior during lane departure warnings (LDW) in two commercial automotive systems utilizing on-road data. Alerts from the systems, one using auditory and the other haptic LDWs, were monitored during highway driving conditions. LDW events were monitored during periods of single-task driving and dual-task driving. Dual-task periods consisted of the driver interacting with the vehicle’s factory infotainment system or a smartphone to perform secondary visual-manual (e.g., radio tuning, contact dialing, etc.) or auditory-vocal (e.g. destination address entry, contact dialing, etc.) tasks.