Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

Bayesian kriging regression for the accuracy improvement of beam modeled T-junctions of buses and coaches structures with a methodology based on FEM behavioral analysis

This paper has been developed in the framework of the alternative beam T-junction solution previously propoused by the authors (Alcalá et al., 2013), with the scope of optimizing the behavior of buses and coaches upper structures modeled with beam type elements. The alternative beam T-junction model proposed by the authors, had a total of six elastic elements at the junction level allowing to modify the localized rigidity of any modeled T-junctions, therefore improving their behavior and avoiding the well known rigidity issue these elements have. A fundamental aspect behind the use of these alternative beam models is related to the necessity of correctly estimating the rigidity values of the elastic elements for each modeled T-junction. In this context, we propose applying a surrogate model for the reference calculations which is based on statistical Bayesian kriging predictors. Statistical predictions have the added value, with respect to deterministic solutions, of providing a quantification of uncertainty. The development of the kriging predictors has required the application of a statistical methodology including computer experiment design, computation of moments of inertia, graphical and ANOVA type sensitivity analysis and Monte Carlo computation of Bayesian inference. The results of the application have been a very satisfactory trade-off between accuracy of approximation (prediction) and computational cost. The surrogate kriging models also provide an useful tool for a better understanding of the input-output relationships involved in the reference computations.     

Evaluation of safety benefits of automatic crash information notification systems on freeways

The automatic crash information notification system (ACINS) is an effective technology to enhance the potential for saving crash victims by reducing the crash response time (CRT) of emergency medical services. Shorter CRT results in a greater potential to save the lives and to alleviate the severity of injuries for crash victims. To fully operate the ACINS, reliable assessments of the safety benefits would be needed for justifying public investment. This study proposed a methodology for quantifying the effectiveness of the ACINS and applied the methodology to the Korean freeway system. The proposed methodology consists of three steps. The first step is to develop a statistical model for predicting injury severity of crash victims using ordered logistic regression. The second step is to estimate the amount of reduced CRT by applying ACINS. The effectiveness of the ACINS, which are defined as the number of reduced fatalities and severe injuries, were evaluated with the consideration of the market penetration rate (MPR) in third step. It has been found that approximately 9.4–15.4% of fatalities can be reduced with 100% MPR when the proposed methodology is applied to 2011 freeway crash data. The outcomes of this study support decision making for public investments and for establishing relevant traffic safety policies.     

Robustness analysis of ESC ECU to characteristic variation of vehicle chassis components using HILS technique

The ESC system, since its introduction in the mid 90s, has greatly contributed to prevention of vehicle accidents with its capability of maintaining vehicle stability in severe driving conditions. Due to its significant advantages, many nations are now adopting regulations that mandate installation of the ESC system in all classes of passenger vehicles — from mini to luxury. Accordingly it became important to know whether an ESC ECU can yield good performance on a wide range of vehicle parameter changes. In this paper, robustness analysis was conducted to study how characteristic variation of the main chassis components affect the performance of the ESC ECU. This analysis was carried out using a HILS system built on an actual ESC ECU. The variation range of each chassis component was carefully selected considering the component’s design criteria adopted in automotive industries. Based upon the robustness analysis results, the allowable variation ranges of the chassis components for ensuring sound performance of an ESC ECU were proposed.     

Dynamics analysis of an omni-directional vehicle

In a modern society, traffic congestion is a major problem in every metropolis. To solve the problem of traffic congestion an innovative omni-directional vehicle is proposed. This research has mainly focussed on developing the comprehensive vehicle dynamics model for an omni-directional road vehicle. Then the stability of the vehicle in the yaw plane was analysed under different scenarios based on the response of the side slip angle. Finally an active steering PID controller was suggested and which, according to the results of the simulation, improved the yaw plane stability.     

Nonlinear sliding mode observer for exhaust manifold pressure estimation in a light-duty diesel engine

In this paper, a sliding mode observer is proposed to estimate exhaust pressure for a diesel engine equipped with variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems. Since the exhaust pressure directly affects generation of the VGT power and the EGR rate in the cylinder, the exhaust pressure information is important for precise control of the VGT and EGR systems. In order to estimate the exhaust pressure accurately, a dynamic model of intake and exhaust pressure was derived. Furthermore, the mass flow rate and temperature of the air system in the diesel engines were modeled by consideration of physical phenomena and the thermodynamic law. Based on the developed models, a nonlinear sliding mode observer was designed to estimate the exhaust pressure. Convergence of the proposed observer was verified by the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated with the engine experiments. The experimental results show that the observer estimates the exhaust pressure accurately in both steady and transient engine operating conditions. Moreover, as a case study, the estimation results of the proposed observer could be applied for detecting a fault of the EGR system. The fault of the EGR system was detected precisely using the estimation result and the limited sensor information in mass-produced engines.     

Development of an autonomous braking system using the predicted stopping distance

An autonomous braking system is designed using the prediction of the stopping distance. The stopping distance needs to be determined by considering several factors such as the desired deceleration and the speed of the hydraulic brake actuator. In particular, the actuator speed is very critical because it affects the shape of the deceleration response and it determines the accuracy of the predicted stopping distance. The autonomous braking control algorithm is designed based on the predicted stopping distance. The proposed autonomous braking system has been validated in autonomous vehicle tests and demonstrates that the subject vehicle can avoid the collision effectively.     

Robust design optimization of frontal structures for minimizing injury risks of Flex Pedestrian Legform Impactor

The Flexible Pedestrian Legform Impactor (Flex-PLI) consisting of a flexible femur and tibia will be tested for pedestrian protection by Euro NCAP within the next couple of years as a potential replacement for the Transport Research Laboratory (TRL) legform impactor. The injury risks that are measured when using Flex-PLI are the elongation of the anterior/posterior cruciate ligament (A/PCL), elongation of the medial collateral ligament (MCL), and tibia bending moment (TBM). In this study, we used a correlated computer-aided engineering (CAE) model to conduct a contribution analysis of each injury with regard to the changes in the location of the frontal structures based on the results of a design of experiments (DOE) and analysis of variance (ANOVA). The frontal structures that were selected as control factors were the energy absorber (EA), lower bumper stiffener (LBS), and hood angle. A kriging interpolation model was developed using the DOE results, and its results were compared with those of the CAE model. Furthermore, for robust design optimization, the speed and height of Flex-PLI were used as the noise factors. Finally, a robust design optimization was carried out using the optimal combination of the discrete control factors for minimizing MCL elongation.     

Abnormality detection via SVDD technique of motor-generator system in HEV

This paper introduces a method to detect abnormality of MGS (Motor-Generator System) in HEV (Hybrid Electric Vehicle) using its temperature. The MGS in HEV consists of two Motor-Generators (MG1, MG2), Compound Gear Unit, and etc. The MG1 is to act as a generator in conventional internal combustion engine. And the MG2 is an electric motor to rotate wheel of vehicle using saved electricity in battery or using produced electricity via the MG1. In case of overheating, the electric motors are easily damaged because resistance of wires in motor is abnormally changed. Therefore, detection of abnormally changed temperature in motors (MG1 and MG2) is essential. In this study, the temperature distribution of two Motor-Generators is observed simultaneously in 2-dimensional space. A boundary region of normal operation temperature of two motors is obtained via SVDD technique utilizing Gaussian kernel, one of the most widely being used Mercer kernels. Linear SVDD technique generates boundary of exact ball shape, however SVDD technique using Gaussian kernel can generate nonlinear boundary of distorted ball shape. Abnormality boundary comparison is made between the obtained boundary via SVDD technique and those obtained from conventional temperature range checking method. In order to compare the performance of proposed method, the actual vehicle operation data in excessive driving condition on mountain road is adopted. In verification, simulation shows that warning time due to proposed method is faster and more efficient than those due to conventional method. It is also shown that the reliability of the Motor-Generator System can be improved by using the proposed abnormality detection method.     

Experimental investigation of B20 combustion and emissions under various intake conditions for low-temperature combustion

Recently, biodiesel has emerged as an alternative fuel for achieving low-temperature combustion (LTC). Several articles in the literature have showed that oxygenated biofuels, including biodiesel, can improve combustion stability under high exhaust gas recirculation (EGR) operation, which is considered to be necessary for the removal of nitric oxides (NOx). The objective of this study was to investigate the performance and emissions of 20% biodiesel blended diesel fuel (B20) at various intake pressures and oxygen concentration levels to characterize the fuel for LTC application. The experimental investigation of B20 was carried out using a single-cylinder engine (SCE) at 1400 rpm and 50% load condition. A set of critical flow orifices with synthetic EGR was employed to simulate various intake pressures and EGR levels. The behavior of the B20 was first characterized under various intake conditions. The results showed that with high oxygen intake, B20 exhibited combustion and emission levels that were very similar to conventional diesel. However, B20 reduced combustion deterioration while exhibiting lower carbon monoxide (CO) and hydrocarbon (HC) emissions than diesel under low oxygen intake conditions.