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Steer-by-wire System is a new conception for steering system, which eliminates those mechanical linkages between hand steering wheel and front wheels, and communicates among the driver and wheels by signals and controllers. All these facilities improve the safety and conformability of the vehicle system and get rid of the mechanical constricts. This paper proposed three vehicle stability control strategies, including front wheel control, yaw rate feedback control and yaw rate& acceleration feedback control. We compared these three control methods by simulation and simulator tests. We also studied the integrated control algorithm of Steer-by-Wire System and 4WS, and compared with 2WS for SBW and the classical 4WS.

Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic, therefore, is critically important for autonomous driving simulation on threat assessment, trajectory planning, etc. Traditionally when modeling traffic, the motion of traffic vehicles is often considered to be deterministic and modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic considering its motion uncertainties, based on Gaussian process (GP).

The effects of different biodiesel blends on engine-out emissions under various transient conditions were investigated in this study using fast response diagnostic equipment. The experimental work was conducted on a modern 3.0 L, V6 high pressure common rail diesel engine fuelled with mineral diesel (B0) and three different blends of rapeseed methyl esters (RME) (B30, B60, B100 by volume) without any modifications of engine parameters. DMS500, Fast FID and Fast CLD were used to measure particulate matter (PM), total hydrocarbon (THC) and nitrogen monoxide (NO) respectively. The tests were conducted during a 12 seconds period with two tests in which load and speed were changed simultaneously and one test with only load changing. The results show that as biodiesel blend ratio increased, total particle number (PN) and THC were decreased whereas NO was increased for all the three transient conditions.

Utilization of direct injection systems is one of the most promising technologies for fuel economy improvement for SI engine powered passenger cars. Engine performance is essentially influenced by the characteristics of the injection equipment. This paper will present CFD analyses of a swirl type GDI injector carried out with the Multiphase Module of AVL's FIRE/SWIFT CFD code. The simulations considered three phases (liquid fuel, fuel vapor, air) and mesh movement. Thus the transient behavior of the injector can be observed. The flow phenomena known from measurement and shown by previous simulation work [2, 7, 10, 11] were reproduced. In particular the simulations shown in this paper could explain the cause for the outstanding atomization characteristics of the swirl type injector, which are caused by cavitation in the nozzle hole.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

The explosion of big data has created challenges for both cloud-based systems and Autonomous Vehicles (AVs) in data collection and management. The same challenges are now being realized in developing databases for integrated sensors, streaming, real-time and on-demand services in AVs. With just one AV expecting to generate over 30 Terabytes of data a day, modern NoSQL databases provide opportunities to horizontally scale AV data seamlessly. NoSQL provides solutions designed to accommodate a wide variety of data models such as, key-value, document, column and graph databases. Key-value stores are by nature scalable, fast processing, and distribute horizontally. These databases are tasked with handling several data types including IoT, radar, lidar, ultra-sonic sensors, GPS, odometry, and sensor data while providing streaming and real-time services. NoSQL can store and utilize structured, semi-structured, and unstructured data necessary for multimedia storage needs.

Realistic vehicle fuel economy studies require real-world vehicle driving behavior data along with various factors affecting the fuel consumption. Such studies require data with various vehicles usages for prolonged periods of time. A project dedicated to collecting such data is an enormous and costly undertaking. Alternatively, we propose to utilize two publicly available vehicle travel survey data sets. One is Puget Sound Travel Survey collected using GPS devices in 484 vehicles between 2004 and 2006. Over 750,000 trips were recorded with a 10-second time resolution. The data were obtained to study travel behavior changes in response to time-and-location-variable road tolling. The other is Atlanta Regional Commission Travel Survey conducted for a comprehensive study of the demographic and travel behavior characteristics of residents within the study area.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

In the intelligent airbag system, the detection accuracy of occupant position is the precondition and plays a vital role to control airbag detonation time and inflated strength during the crash. Through accurately analyzing the seat surface pressure distributions of different occupant sitting position and types, an occupant position recognition approach which purely uses occupant pressure distribution information measured by seat pressure sensors is presented with the method of Support Vector Machine. In the end, the distribution samples with different occupant sitting position and types are used to train and test the recognition approach, and the good validity and accuracy are shown in the experiments.

In this paper, a computer model of a multi-purpose vehicle (MPV) is built to study vehicle ride comfort by multi-body system dynamic theory. Virtual test rigs are developed to perform natural body frequency tests and random road input tests on the complete vehicle multi-body dynamic model. By comparing simulation results with field test results, the accuracy of the model is validated and the feasibility of virtual test rigs is established.

The direct injection spray-wall interactions were investigated experimentally using high-speed laser-sheet imaging, shadowgraphy, wetted footprints and phase Doppler interferometry techniques. A narrow-cone high-pressure swirl injector is used to inject iso-octane fuel onto a plate, at three different impact angles inside a pressurized chamber. Heated air and plate conditions were compared with unheated cases. Injection interval was also varied in the heated case to compare dry- and wet- wall impingement behaviors. High-speed macroscopic Mie-scattering images showed that presence of wall and air temperature has only minor effect on the bulk spray structure and penetration speed for the narrow-cone injector tested. The overall bulk motions of the spray plume and its spatial position at a given time are basically unaffected until a few millimeters before impacting the wall.

Two-dimensional pulse-laser Mie scattering visualization of the direct-injection gasoline fuel sprays and wall impingement processes was carried out inside a single-cylinder optically accessible engine under motoring condition. The injectors have been first characterized inside a pressurized chamber using identical technique, as well as high-speed microscopic visualization and phase Doppler measurement techniques. The effects of injector cone angle, location, and injection timings on the wall impingement processes were investigated. It was found that the fuel vaporization is not complete at the constant engine speed tested. Fuel spray droplets were observed to disperse wider in the motored engine when compared with an isothermal quiescent ambient conditions. The extent of wall-impingement varies significantly with the injector mounting position and spray cone angle; however, its effect can be reduced to some extent by optimizing the injection timing.

Combustion visualizations were carried out in a constant volume vessel to study the partially premixed combustion of a gasoline-like fuel using high speed imaging. The test fuel (G80H20) is composed by volume 80% commercial gasoline and 20% n-heptane. The effects of ambient gas composition, ambient temperature and injection pressure on G80H20 combustion characteristics were analyzed. Meanwhile, a comparison of the EGR effect on combustion process between G80H20 and diesel was made. Four ambient gas conditions that represent the in-cylinder gas compositions of a heavy-duty diesel engine with EGR ratios of 0%, 20%, 40% and 60% were used to simulate EGR conditions. Variables also include two ambient temperature (910K and 870K) and two injection pressure (20 MPa and 50 MPa) conditions.

Advanced diesel engines developed for the commercial market need to be adapted to the military requirements by OPERAS (Optimizing the injection pressure P, the Exhaust gas recirculation E, injection events Retard and/or Advance and the swirl ratio S). The different after treatment devices, already used or expected to be applied to diesel engines, require feed gases of appropriate properties for their efficient operation. To produce these gases some OPERAS are needed to control the diesel combustion process. Since military vehicles do not need the after treatment devices, the OPERAS of the commercial engines should be modified to meet the military requirements for high power density, better fuel economy, reduction of parasitic losses caused by the cooled EGR system, and reduction of invisible black and white smoke in the field.