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.     

Transient modeling and validation of lithium ion battery pack with air cooled thermal management system for electric vehicles

A transient numerical model of a lithium ion battery (LiB) pack with air cooled thermal management system is developed and validated for electric vehicle applications. In the battery model, the open circuit voltage and the internal resistance map based on experiments are used. The Butler-Volmer equation is directly considered for activation voltage loss estimation. The heat generation of cells and the heat transfer from cells are also calculated to estimate temperature distribution. Validations are conducted by comparison between experimental results at the cell level and the pack level. After validations, the effects of module arrangement in a battery pack are studied with different ambient temperature conditions. The configuration that more LiB cells are placed near the air flow inlet is more effective to reduce the temperature deviation between modules.     

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.     

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.     

Experimental approach to developing human driver models considering driver’s human factors

A new approach to develop human driver models (HDMs) is proposed in accordance with the drivers’ generic human factors, i.e., gender, age, and experience, to develop more realistic vehicle simulations. The HDMs consist of three independent and stepwise models with functioning driver’s information processing stages based on the human factors: constructing drivers’ preview distance (PVD) models as a ‘cognition process’, implementing a finite preview optimal control algorithm as a ‘decision process’, and differentiating an ‘operation process’ according to neuromuscular efficiency. Eight different groups of 65 drivers with a 2 × 2 × 2 within-subject design participated in both the PVD estimates and neuromuscular efficiency tests to develop a set of statistically different HDMs. Regarding the preview distance models, an analysis of covariance (ANCOVA) procedure was adopted with two covariates (i.e., vehicle velocity and road curvature), while analyses of variance (ANOVAs) were performed on the neuromuscular efficiency parameters. The ANCOVA procedure produced eight significantly different cognition processes, whereas the ANOVAs revealed gender differences for the drivers’ neuromuscular systems. Moreover, an integrated vehicle simulation was configured with the HDMs using Carsim and Simulink software to observe the differential effects of both the cognition and operation processes on a double-lane-change (DLC) maneuver. During the simulations, gender differences in real-world DLC tests were also identified, especially between the male-oldexpert and the female-young-novice HDMs. The results presented in this study suggest that differentiating HDMs according to human factors is an essential process when utilizing vehicle simulations in the early stage of developing an intelligent vehicle system.     

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.     

Evaluation of driver’s mental workload by facial temperature and electrodermal activity under simulated driving conditions

The purpose of this study was to evaluate the mental workload of increasing driving speed, from 60 km/h to 180 km/h, when operating a driving simulator. The evaluation, based on changes in facial temperature and electrodermal activity, showed that the difference between nose and forehead temperature increased, that the skin potential level decreased, and that the skin conductance level increased. Monitoring facial temperature and electrodermal activity were both found to be effective in evaluating the mental workload involved.