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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.

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.

During recent years, the deterioration of greenhouse phenomenon, in conjunction with the continuous increase of worldwide fleet of vehicles and crude oil prices, raised heightened concerns over both the improvement of vehicle mileage and the reduction of pollutant emissions. Diesel engines have the highest fuel economy and thus, highest CO2 reduction potential among all other thermal propulsion engines due to their superior thermal efficiency. However, particulate matter (PM) and nitrogen oxides (NOx) emissions from diesel engines are comparatively higher than those emitted from modern gasoline engines. Therefore, reduction of diesel emitted pollutants and especially, PM and NOx without increase of specific fuel consumption or let alone improvement of diesel fuel economy is a difficult problem, which requires immediate and drastic actions to be taken.

This work determined the suitability of two silicon carbide (SiC) monoliths (one regular and one coated with a micromembrane), as well as a coated cordierite monolith for use as aerodynamically regenerated particulate filters for diesel engines. These ceramic honeycomb monoliths were tested for their filtration efficiency, their post filtration particulate size distribution and their ability to be aerodynamically regenerated at pre-selected operating temperatures (200, 300 and 400°C). Through combined laboratory and field testing, the uncoated silicon carbide filter produced the most satisfactory results in all of these tests. This filter resulted in excellent regeneration characteristics while maintaining the highest filtration efficiencies at all particle sized tested. All filters were found to clean effectively at all temperatures. However, upon normalization with the volumetric flow rate through the monolith, it was found that the filters were most thoroughly cleaned at 400°C.

This manuscript describes recent design developments and extensive testing of ART-EGR systems (Aerodynamically Regenerated Traps with Exhaust Gas Recirculation). Such systems have been proven to drastically reduce the emissions of particulates (by up to 99%), NOx (by up to 50%) and unburned volatile hydrocarbons (typically by 30-70%). Such emission reductions, however, are associated with increased fuel consumption. In this work extensive design modifications were implemented to minimize the fuel consumption penalty caused by the ART-EGR system. All plumbing and valving gear has been redesigned to maximize the flow of regeneration air through the primary filter monolith. An expansion chamber integrated with the ART system, downstream of the primary filter, increased the flow of the regeneration air. At the exit of the expansion chamber, a secondary filter/burner retained and oxidized the soot.

Simulation of chemical kinetic processes in combustion engine environments has become ubiquitous towards the understanding of combustion phenomenology, the evaluation of controlling parameters, and the design of configurations and/or control strategies. Such calculations are not free from error however, and the interpretation of simulation results must be considered within the context of uncertainties in the chemical kinetic model. Uncertainties arise due to structural issues (e.g., included/missing reaction pathways), as well as inaccurate descriptions of kinetic rate parameters and thermochemistry. In fundamental apparatuses like rapid compression machines and shock tubes, computed constant-volume ignition delay times for simple, single-component fuels can have variations on the order of factors of 2-4.

Recent advances in fuel-cell technology and low-emission, direct-injection spark-ignition and diesel engines for vehicles could significantly change the transportation vehicle power plant landscape in the next decade or so. This paper is a scoping study that compares total fuel cycle options for providing power to personal transport vehicles. The key question asked is, “How much of the energy from the fuel feedstock is available for motive power?” Emissions of selected criteria pollutants and greenhouse gases are qualitatively discussed. This analysis illustrates the differences among options; it is not intended to be exhaustive. Cases considered are hydrogen fuel from methane and from iso-octane in generic proton-exchange membrane (PEM) fuel-cell vehicles, methane and iso-octane in spark-ignition (SI) engine vehicles, and diesel fuel (from methane or petroleum) in direct-injection (DI) diesel engine vehicles.