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The goal of this interview analysis was to explore and document the perceptions of two unique lane centering systems (S90’s Pilot Assist and CT6’s Super Cruise). Both systems offer a similar type of functionality (adaptive cruise control and lane centering), but have significantly different design philosophies and HMI (Human-Machine Interface) implementations. Twenty-four drivers drove one of the two vehicle models for a month as part of a field operational test (FOT) study. Upon vehicle return, drivers took part in a 60-minute semi-structured interview covering their perceptions of the vehicle’s various advanced driver-assistance systems (ADAS). Transcripts of the interviews were coded by two researchers, who tagged each statement with relevant system and perception code labels. For analysis, the perception codes were grouped into larger thematic bins of safety, comfort, driver attention, and system performance.

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.

Gas-pressurized space suits are highly resistive to astronaut movement, and this resistance has been previously explained by volume and/or structural effects. This study proposed that an additional effect, pressure effects due to compressing/expanding the internal gas during joint articulation, also inhibits mobility. EMU elbow torque components were quantified through hypobaric testing. Structural effects dominated at low joint angles, and volume effects were found to be the primary torque component at higher angles. Pressure effects were found to be significant only at high joint angles (increased flexion), contributing up to 8.8% of the total torque. These effects are predicted to increase for larger, multi-axis joints. An active regulator system was developed to mitigate pressure effects, and was found to be capable of mitigating repeated pressure spikes caused by volume changes.

A new monitoring system predicts the progression of welding temperature fields during resistance spot welding. The system captures welding voltages and currents to predict contact diameters and simulate temperature fields. The system accurately predicts fusion lines and heat-affected zones. Accuracy holds even for electrode tips used for a few thousand welds of zinc coated steels.

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.

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.

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.

Higher compression ratio and turbocharging, with engine downsizing can enable significant gains in fuel economy but require engine operating conditions that cause engine knock under high load. Engine knock can be avoided by supplying higher-octane fuel under such high load conditions. This study builds on previous MIT papers investigating Octane-On-Demand (OOD) to enable a higher efficiency, higher-boost higher compression-ratio engine. The high-octane fuel for OOD can be obtained through On-Board-Separation (OBS) of alcohol blended gasoline. Fuel from the primary fuel tank filled with commercially available gasoline that contains 10% by volume ethanol (E10) is separated by an organic membrane pervaporation process that produces a 30 to 90% ethanol fuel blend for use when high octane is needed. In addition to previous work, this paper combines modeling of the OBS system with passenger car and medium-duty truck fuel consumption and octane requirements for various driving cycles.

In today's business climate rapid access to, and implementation of, new technology is essential to enhance competitive advantage. In the past, universities have been used for research contracts, but to fully utilize the intellectual resources of education institutions, it is essential to approach these relationships from a new basis: alliance. Alliances permit both parties to become active participants and achieve mutually beneficial goals. This paper will examine the drivers and challenges for industrial -- university alliances from both the industrial and academic perspectives.

This paper describes the use of a modified quasi-dimensional spark-ignition engine simulation code to predict the extent of cycle-to-cycle variations in combustion. The modifications primarily relate to the combustion model and include the following: 1. A flame kernel model was developed and implemented to avoid choosing the initial flame size and temperature arbitrarily. 2. Instead of the usual assumption of the flame being spherical, ellipsoidal flame shapes are permitted in the model when the gas velocity in the vicinity of the spark plug during kernel development is high. Changes in flame shape influence the flame front area and the interaction of the enflamed volume with the combustion chamber walls. 3. The flame center shifts due to convection by the gas flow in the cylinder. This influences the flame front area through the interaction between the enflamed volume and the combustion chamber walls. 4. Turbulence intensity is not uniform in cylinder, and varies cycle-to-cycle.

This paper describes a three component strain gage balance designed to measure aerodynamic forces exerted on small automobile models when subjected to turbulence in an experimental wind tunnel. The instrument is described and the details of obtaining values with it are fully explained. Although tests were conducted on these models at quarter-scale Reynolds number, results agree closely with similar tests on larger models. The balance makes practical some unusual preliminary investigations before developing full-scale prototypes.

A linearized lumped parameter heat balance model was developed and is discussed for the general case of resistance welding to see the effects of each parameter on the lobe shape. The parameters include material properties, geometry of electrodes and work piece, weld time and current, and electrical and thermal contact characteristics. These are then related to heat dissipation in the electrodes and the work piece. The results indicate that the ratio of thermal conductivity and heat capacity to electrical resistivity is a characteristic number which is representative of the ease of spot weldability of a given material. The increases in thermal conductivity and heat capacity of the sheet metal increase the lobe width while increases in electrical resistivity decrease the lobe width. Inconsistencies in the weldability of thin sheets and the wider lobe width at long welding times can both be explained by the heat dissipation characteristics.

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.

A one-dimensional model for crevice HC post-flame oxidation is used to calculate and understand the effect of operating parameters and fuel type (propane and isooctane) on the extent of crevice hydrocarbon and the product distribution in the post flame environment. The calculations show that the main parameters controlling oxidation are: bulk burned gas temperatures, wall temperatures, turbulent diffusivity, and fuel oxidation rates. Calculated extents of oxidation agree well with experimental values, and the sensitivities to operating conditions (wall temperatures, equivalence ratio, fuel type) are reasonably well captured. Whereas the bulk gas temperatures largely determine the extent of oxidation, the hydrocarbon product distribution is not very much affected by the burned gas temperatures, but mostly by diffusion rates. Uncertainties in both turbulent diffusion rates as well as in mechanisms are an important factor limiting the predictive capabilities of the model.

Lubricant viscosity along the engine cylinder liner varies by an order of magnitude due to local temperature variation and vaporization effects. Tremendous potential exists for fuel economy improvement by optimizing local viscosity variations for specific operating conditions. Methods for analytical estimation of friction and wear in the power-cylinder system are reviewed and used to quantify opportunities for improving mechanical efficiency and fuel economy through lubricant formulation tailored specifically to liner temperature distributions. Temperature dependent variations in kinematic viscosity, density, shear thinning, and lubricant composition are investigated. Models incorporating the modified Reynolds equation were used to estimate friction and wear under the top ring and piston skirt of a typical 11.0 liter diesel 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.

The NOx emission and knock characteristics of a PFI engine operating on ethanol/gasoline mixtures were assessed at 1500 and 2000 rpm with λ =1 under Wide-Open-Throttle condition. There was no significant charge cooling due to fuel evaporation. The decrease in NOx emission and exhaust temperature could be explained by the change in adiabatic flame temperature of the mixture. The fuel knock resistance improved significantly with the gasohol so that ignition could be timed at a value much closer or at MBT timing. Changing from 0% to 100% ethanol in the fuel, this combustion phasing improvement led to a 20% increase in NIMEP and 8 percentage points in fuel conversion efficiency at 1500 rpm. At 2000 rpm, where knocking was less severe, the improvement was about half (10% increase in NIMEP and 4 percentage points in fuel conversion efficiency).

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.