Abstract

Recent developments of information technology and mobile lifestyle have forced drivers to multitask while they drive. The in-vehicle “infotainment” technology is already taking its place in the transformation of vehicles towards more intelligent and interactive devices rather than staying as mere transportation convenience. This transformation has several advantages such as easy route navigation, real-time traffic information, and staying connected with work or people while traveling. However, it has several drawbacks concerning the impact on driver cognitive load and attention sources. Therefore, it is crucial to take advantage of state-of-the-art in-vehicle technology to produce counter-measure systems that monitor the driver status and reduce driver workload adaptively depending on the context. In recognition and analysis of the driving context together with driver status monitoring, computer vision applications supply crucial information both in the vehicle (i.e., driver head and eye tracking) and out of the vehicle (i.e., lane, pedestrian, and vehicle detection and tracking, and road sign recognition). In this chapter, we provide a broad range of computer vision applications for CA-IVS from the literature and our previous studies, and we report our current research efforts. Document type: Part of book or chapter of book

Abstract

fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults. "p"QC 20140509. Updated from manuscript to article in journal"/p""p""/p""p"Funder: Swedish Hybrid vehicle Center

Abstract

Computational human body models are widely used for automotive crashsafety research and design and as such have significantly contributed to a reduction of traffic injuries and fatalities. Currently crash simulations are mainly performed using models based on crash-dummies. However crash dummies differ significantly from the real human body and moreover crash dummies are only available for a limited set of body sizes. Models of the real human body offer some promising advantages including the prediction of injury mechanisms and injury criteria. In this paper, a review will be given of a number of developments in the field of occupant crash simulations in the past 40 years. Topics presented include history of occupant crash simulation codes, human body geometry, human body material modelling and model quality rating. A discussion on foreseen future developments in this field will conclude this paper. © 2005 Springer.

Abstract

[EN] Driving a private vehicle represents nowadays one of the most viable ways by which disabled drivers can maintain their mobility and transport conditions. To do so it is necessary to adapt a vehicle to the user's residual capabilities. Nevertheless the technical aids installed in the vehicles must fulfill the same safety requirements as standard vehicles. This paper shows the results of a research project held at the Automobile Laboratory of the Technical University of Valencia (Spain), the main objective being to generate basic design criteria for improving the passive safety behaviour of the adapted vehicle under different impact conditions. The research project whose partial results are shown in this paper was developed by the Automobile Laboratory of the Technical University of Valencia (Spain), under the auspices of the Spanish Ministry of Science and Innovation (Ref. TRA2005-08649) during the years 2006–2008. Dols Ruiz, JF.; Masiá Vañó, J.; Eixerés Tomás, B. (2012). Passive Safety evaluation in driving adapted vehicles. International Journal of Vehicle Safety. 6(1):77-89. doi:10.1504/IJVS.2012.048534 S 77 89 6 1

Abstract

Road crash deaths and injuries in Nepal have been on a sharp upward trajectory since the early 2000s. In fi scal year 2017–18, 2,541 road deaths were offi cially reported in Nepal, which is equivalent to a fatality rate of 8.59 per 100,000 population. In the same period, 4,144 serious injury and several minor injury victims were also offi cially reported. However, according to World Health Organization data the estimated fatality rate in 2016 was 15.9 per 100,000 population, which is nearly double the offi cial estimate. In 2016, vulnerable road users (pedestrians, cyclists, and motorcyclists) accounted for around 72 percent of all road fatality victims, among the highest levels in the region, with pedestrians accounting for half of these. Road deaths have a disproportionate impact on the young, working age population. About 40 percent of people killed on Nepal’s roads in 2017 –18 were less than 26 years old. In 2016, transport injuries were the second leading cause of death among men aged 15–49-years.