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A novel system for the forming of three dimensional sheet metal parts is described that can form a variety of part shapes without the need for fixed tooling, and given only geometry (CAD) information about the desired part. The central elements of this system are a tooling concept based on a programmable discrete die surface and closed-loop shape control. The former give the process the degrees of freedom to change shape rapidly, and the latter is used to insure that the correct shape is formed with a minimum of forming trials. A 540 kN (60 ton) lab press has been constructed with a 0.3 m (12 in) square pair of discrete tools that can be rapidly re-shaped between forming trials. The shape control system uses measured part shapes to determine a shape error and to correct the tooling shape. This correction is based on a unique “Deformation Transfer Function” approach using a spatial frequency decomposition of the surface.

This paper quantifies the potential of electric propulsion systems to reduce petroleum use and greenhouse gas (GHG) emissions in the 2030 U.S. light-duty vehicle fleet. The propulsion systems under consideration include gasoline hybrid-electric vehicles (HEVs), plug-in hybrid vehicles (PHEVs), fuel-cell hybrid vehicles (FCVs), and battery-electric vehicles (BEVs). The performance and cost of key enabling technologies were extrapolated over a 25-30 year time horizon. These results were integrated with software simulations to model vehicle performance and tank-to-wheel energy consumption. Well-to-wheel energy and GHG emissions of future vehicle technologies were estimated by integrating the vehicle technology evaluation with assessments of different fuel pathways. The results show that, if vehicle size and performance remain constant at present-day levels, these electric propulsion systems can reduce or eliminate the transport sector's reliance on petroleum.

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.

Advances in avionics and display technology are significantly changing the cockpit environment in current transport aircraft. The MIT Aeronautical Systems Lab (ASL) has developed a part-task flight simulator specifically to study the effects of these new technologies on flight crew situational awareness and performance. The simulator is based on a commercially-available graphics workstation, and can be rapidly reconfigured to meet the varying demands of experimental studies. The simulator has been successfully used to evaluate graphical microburst alerting displays, electronic instrument approach plates, terrain awareness and alerting displays, and ATC routing amendment delivery through digital datalinks.

Multi-target recognition technology plays an important role in the field of intelligent driving. In this paper, we propose a novel multi-target recognition algorithm with high accuracy and efficiency. We design a time series based recurrent neural network that integrates historical appearance information on the timeline, which can effectively improve the recognition accuracy. The target appearance characteristics extracted from the feature fusion network are then sent to the recursive neural network with the function of long-term and short-term memory for prediction, extending the learning and analysis of the neural network to the space-time domain. After the LSTM interprets advanced visual features, time series based regression is used as an appearance model to regress features to a particular visual element position through preliminary position inference. We evaluate our proposed algorithm on the KITTI data set and a large number of real scene experiments.

This paper presents a numerical model of the rotational and lateral dynamics of the piston (secondary motion) and piston slap in mixed lubrication. Piston dynamic behavior, frictional and impact forces are predicted as functions of crank angle. The model considers piston skirt surface waviness, roughness, skirt profile, thermal and mechanical deformations. The model considers partially-flooded skirt and calculates the pressure distributions and friction in the piston skirt region for both hydrodynamic and boundary lubrication. Model predictions are compared with measurements of piston position using gap sensors in a single-cylinder engine and the comparison between theory and measurement shows remarkable agreement.

A twin-land oil control ring (TLOCR) model is used to evaluate TLOCR friction and the results are compared to the experiment measurement in a single cylinder floating liner engine under motoring condition. The model is based on a correlation between the hydrodynamic pressure and film thickness, which is generated using a deterministic model. The well-known three-regime lubrication is predicted with the model for ring with different ring tensions under various engine running conditions. A good match is found for the model and experiment results.

A fuel volatility model based on the major species present in the fuel has been formulated. The model accurately predicts the ASTM distillation curves and Reid Vapor Pressure for hydrocarbon fuels. The model may be used to assess the fuel effects on the extent of evaporation and the vapor composition in the mixture preparation process.

MIT Lincoln Laboratory is tasked by the U.S. Federal Aviation Administration to investigate the use of the NEXRAD polarimetric radars* for the remote sensing of icing conditions hazardous to aircraft. A critical aspect of the investigation concerns validation that has relied upon commercial airline icing pilot reports and a dedicated campaign of in situ flights in winter storms. During the month of February in 2012 and 2013, the Convair-580 aircraft operated by the National Research Council of Canada was used for in situ validation of snowstorm characteristics under simultaneous observation by NEXRAD radars in Cleveland, Ohio and Buffalo, New York. The most anisotropic and easily distinguished winter targets to dual pol radar are ice crystals.

Low volume manufacturing has become increasingly important for the automotive industry. Globalization trends have led automakers and their suppliers to operate in developing regions where minimum efficient scales can not always be achieved. With proper maintenance, standard cast iron stamping tools can be used to produce millions of parts, but require large investments. Thus at high production volumes, the impact of the tooling investment on individual piece costs is minimized. However, at low volumes there is a substantial cost penalty. In light of the trends towards localized manufacturing and relatively low demands in some developing markets, low cost stamping tools are needed. Several alternate tooling technologies exist, each of which require significantly lower initial investments, but suffer from greatly reduced tool lives. However, the use of these technologies at intermediate to high volumes requires multiple tool sets thus eliminating their cost advantage.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Acquiring drivers’ physiological parameters is helpful for driver assistance systems to reliably estimate drivers’ comprehensive mental states, but traditional methods of measurements have to intrusively attach sensors or fasten electrodes to the body. Intrusive methods of measurement are acceptable in experimental settings but unacceptable as routine operation in daily driving. In this paper, the Smart Wheel System is introduced. The Smart Wheel System is a prototype system that collects the following cognition/emotion physiological signals: blood volume pulse, skin conductance, skin temperature, and respiration wave. It uses embedded sensors and electrodes in steering wheel and seat belt with near-zero interference. Due to the fact that hand and body movements cause temporary gap in the real-time data stream, gripping force and gripping position are collected as indicative data to compensate breaks in the physiological data stream.

Drowsy driving is a serious issue in modern transportation. Our review of current ways to detect driver drowsiness revealed that electroencephalography (EEG) has the potential to achieve excellent performance in driver drowsiness recognition. This paper presents an experimental study for finding EEG features that are sensitive to a driver’s drowsiness, yet robust to noise signals. We recorded participants’ EEGs in an alert state and a drowsy state for a driving task performed in a driving simulator. Twelve EEG features were analyzed for their power of distinguishing the alert state from the drowsy state. As a conclusion, specific EEG features can be used to detect drivers’ drowsiness. The classification power of the EEG features depends on the scalp locations where the EEG is measured.

The intelligent vehicle driverless technology has become a very hot topic in the past two decades. To solve the road safety problem, which is one of the most important factors inhibiting the development of intelligent vehicle technology, the multi-target tracking system has attracted more and more attention in recent study since it can detect and track multiple objects in traffic scene so as to help the whole driving system plan the safe route. In this work, a novel multi-pedestrian tracking system based on deep neural network is proposed to improve the tracking efficiency while providing high recognition accuracy. The proposed tracking system consists of two parts: 1) pedestrian detector, and 2) pedestrian tracker.

An analysis method for characterizing fuel behavior during spark-ignition engine starting has been developed and applied to several sets of start-up data. The data sets were acquired from modern production vehicles during room temperature engine start-up. Two different engines, two control schemes, and two engine temperatures (cold and hot) were investigated. A cycle-by-cycle mass balance for the fuel was used to compare the amount of fuel injected with the amount burned or exhausted as unburned hydrocarbons. The difference was measured as “fuel unaccounted for”. The calculation for the amount of fuel burned used an energy release analysis of the cylinder pressure data. The results include an overview of starting behavior and a fuel accounting for each data set Overall, starting occurred quickly with combustion quality, manifold pressure, and engine speed beginning to stabilize by the seventh cycle, on average.

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.

A design framework based on the principles of lean manufacturing and axiomatic design was used as a guideline for designing an automotive component manufacturing system. A brief overview of this design decomposition is given to review its structure and usefulness. Examples are examined to demonstrate how this design framework was applied to the design of a gear manufacturing system. These examples demonstrate the impact that low-level design decisions can have on high-level system objectives and the need for a systems-thinking approach in manufacturing system design. Results are presented to show the estimated performance improvements resulting from the new system design.

Ash, primarily derived from diesel engine lubricants, accumulates in diesel particulate filters directly affecting the filter's pressure drop sensitivity to soot accumulation, thus impacting regeneration frequency and fuel economy. After approximately 33,000 miles of equivalent on-road aging, ash comprises more than half of the material accumulated in a typical cordierite filter. Ash accumulation reduces the effective filtration area, resulting in higher local soot loads toward the front of the filter. At a typical ash cleaning interval of 150,000 miles, ash more than doubles the filter's pressure drop sensitivity to soot, in addition to raising the pressure drop level itself. In order to evaluate the effects of lubricant-derived ash on DPF pressure drop performance, a novel accelerated ash loading system was employed to generate the ash and load the DPFs under carefully-controlled exhaust conditions.

Measurements of explosion limits for fuel/air/diluent mixtures compressed by an expanding laminar flame have been made in a constant volume spherical bomb. The fuels studied to date range from butane to octane at fuel/air equivalence ratios from 0.8 to 1.3. The explosion pressures and temperatures range from 10 to 100 atm and 650 to 850 K. The pressure versus time curves show the behavior typical of the two-stage ignition process observed in rapid compression machines. A branched chain kinetic model has been developed to correlate the data. The model has been used to predict both the explosion limits measured in the current bomb experiments and ignition delays measured in prior rapid compression machine experiments. Good agreement between experiment and theory can be achieved with minor adjustment in published rate constants.