Abstract

The project LIFE E-VIA aims to tackle noise pollution from road traffic noise, in a future perspective involving a consistent portion of electric and hybrid vehicles. By combining knowledge of road optimization and tyre development, it will test an optimized solution for reducing noise in urban areas and Life Cycle Cost with respect to actual best practices. The project includes three preparatory actions, consisting of state-of-the-art studies on the components implicated in the issue: the electric vehicles (EVs), the quiet pavement technologies and the tyre role in the context of EVs vs. conventional vehicles. This report refers to project action A1 and provides an overview of the concern of electric vehicles and of their noise emission. It intends to highlight the key aspects to be taken into consideration in order to achieve the most effective and relevant implementation work, in the light of the latest technical and scientific knowledge. In the shift from conventional mobility to electromobility, the present study deals with: the electric vehicle fleet characteristics, the changed driving behaviours induced by the vehicle specificities, the different noise source features with a sharp focus on rolling noise, the changes in the noise perception by the citizens and the consideration of electric vehicles in the noise prediction methods.

Abstract

Text in English; Abstract: English and Turkish Includes bibliographical references (leaves 53-58) ix, 53 leaves Greenhouse gasses is a main thread for global warming and there are several strategies that reducing GHG gasses especially in developing countries as using alternative fuel types, dealing with the congestion, maintaining the steady traffic flow, dealing with the maintenances of vehicle, managing desired speed and the acceleration rates and so on. Eco-Driving is indicating any implementations which enables driving more economic and ecologic style. One of the main concepts of eco-driving is configuring driving behaviour to reduce consumption and emissions. In this thesis, impact of driver's behaviour tried to be investigated by using an instantaneous emission modelling to obtain minimum acceleration deceleration rate in generically created urban network. Sera gazları küresel ısınmanın ana tehdididir ve özellikle gelişmekte olan ülkelerde alternatif yakıt türlerini kullanmak, trafik sıkışıklığını önlemek, trafiğin akıcı bir şekilde devam etmesini sağlamak, araç bakımlarını yönetmek, istenilen hızı çıkmayı yönetmek ve bu hızda devam etmek gibi çeşitli stratejiler ile bu sera gazlarının ulaştırma sektöründe düşürülmesi amaçlanmaktadır. Eko-Sürüş, daha ekonomik ve ekolojik sürüşü mümkün kılan uygulamaların bir bütünüdür. Eko-sürüşün operasyonel olarak temel kavramlarından birisi de sürücü davranışlarının yakıt tüketimi ve emisyonu azaltıcı şekilde düzenlemektir. Bu tezde, kentsel ağdaki bir sürüş parkurunu taklit edici bir parkur dizayn edilerek ve emisyon modeli kullanılarak minimum düzeyde yakıt tüketimi ve emisyon elde edebilmek için burada hızlanma eğrilerinin etkileri değerlendirilmiştir.

Abstract

Novo Mesto is a small town in the Slovenian countryside which is undergoing a new project of road safety. New traffic signals are to be placed in bus stops to give drivers awareness of children standing waiting for the bus. Our mission is to use an eye tracker (the Dikablis device by Ergoneers) to study the reaction of three test drivers against these new signals; if they do detect them or not and if they modify their driving behavior once noticed them. The methodology is to do a first test before the signal placements and another one after, and afterwards compare both situations. Results are satisfying, with high values of reliability, and make us think the new signals will be useful and will contribute to make the road a safer place for children. Outgoing

Abstract

The increased availability of detailed trajectory data sets from naturalistic, observational, and simulation-based studies, is a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and various driving maneuvers. Despite the well-recognized research findings on both crash frequency estimation and traffic conflict analysis carried out over the last decades, only recently researchers have started to study and model the link between the two. This link is typically made by statistical association between aggregated conflicts and crashes, which still relies on crash data and ignores heterogeneity in the estimation procedure. More recently, an extreme value (EV) approach has been used to link the probability of a crash occurrence to the frequency of conflicts estimated from observed variability of crash proximity, using a probabilistic framework and without using crash records. In this study the Generalized Extreme Value distribution used in the block maxima (BM) approach and the Generalized Pareto Distribution used in the peak over threshold approach (POT), are tested and compared for the estimation of head-on collisions in passing maneuvers. The minimum time-to-collision with the opposite vehicle is used in both EV methods. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment was used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary basis. Several two-lane rural highway layouts and traffic conditions were considered in the design of the driving simulator scenarios. Raw data was collected at a resolution of 0.1 s and included the longitudinal and lateral positions, speeds and accelerations of all vehicles in the scenario. From this raw data, both methods were tested for stationary and non-stationary models. The latter allowed not-only for a better modeling performance in estimating the number of expected crashes, but also for a quantified analysis of the detailed driving choices affecting the head-on crash probability during passing maneuvers. The estimation results showed that the BM approach yielded more stable results compared to the POT approach, but the latter was able to produce crash rate estimates more consistently sensitive to the covariates of interest. Finally, the estimated distributions were validated using a second set of data extracted from an additional driving simulator experiment. The results indicate that this is a promising approach for safety evaluation. On-going work of the authors will attempt to generalize this method to other safety measures related to passing maneuvers, test it for the detailed analysis of the effect of demographic factors on passing maneuvers' crash probability and for its usefulness in a traffic simulation environment.

Abstract

Numerous studies have already demonstrated the benefits and social relevance of electric vehicles. But why EVs are not yet visible in our streets? To provide an answer to this question the Flemish government has set up five living labs for the deployment of electric vehicles in 2011. Several studies presented in literature are focused on the consumer and his/her perception, but rarely take into account whether the consumer actually drove an EV. Therefor the iMove- and EVA-platform, as part of The Living Labs, form the ideal environment to investigate the perception of test users. Test drivers were asked to fill-in a same questionnaire before and after the test. Questions included inter alia judging the possible advantages and disadvantages, purchase potential. Many people were interested to test an electric car, although there basic knowledge about EVs was poor. Consumers confirm that the lower cost per kilometer is an important advantage, although it became less crucial after testing. Charging at home is perceived as an important advantage. The limited electric range still remains the main disadvantage, followed by the high purchase price. Consumers clearly underestimated the effect of a limited range. The willingness to purchase an EV within the future is related to the consumer’s idea of when an EV will be a full alternative. The majority consider buying an electric car in the near future (within 4 years). Related to this, one-third of the test population is willing to pay more for an electric car. Document type: Article

Abstract

Purpose The trajectory of a vehicle is comprehensively affected by the interactions between the vehicle, the driving behavior, and the road environment. High-risk driving behaviors and accident-prone road sections can be identified based on the relationship between the trajectory and road geometry. Previous related studies mostly focused on the trajectory deviation at a few points on the road, which cannot capture the continuous variation of the trajectory in an entire curve, and seldom considered the trajectory characteristics along curves with large deflection angles. The aim of this study is to investigate the trajectories passenger cars take on two-lane mountain roads and thus to determine the track patterns and its relevant risks. Methods Field driving experiments were performed on four two-lane mountain highways, and vehicle trajectories under natural driving conditions were acquired. The continuous change in the lateral deviation rate of the trajectory was also determined by putting the measured trajectories into the coordinate frame together with the edge line of roadway. Further, the morphological features of the vehicle trajectory and how it is affected by the highway geometry were analyzed. Results and conclusions The following were observed: i) Typical track patterns were determined according to features of LDRT profiles, four patterns for left-hand bends and five patterns for right-hand bends, which can be used to identify crash prone position and reveal the mechanism of crash. ii) Inertia may cause the vehicle to move too close to the outer side of the curve after a cut, for which reason the driver has to correct the trajectory, although overcorrection may move the vehicle into the oncoming lane. iii) A higher speed at curve entry adopt by the driver could result in a larger encroachment into opposite lane or shoulder. iv) The smaller the radius of the horizontal curve, the more frequently the trajectory entered the oncoming lane. These findings could provide a better understanding of the track behavior of passenger cars, judge the safety implications of driver behavior, and thus identify crash prone positioning and the potential mechanisms of head-on crashes, run-off-road and guardrail collisions. Document type: Article

Abstract

Driving support and cruise assist systems are of growing importance in achieving both road traffic safety and convenience. Such driver support seeks to achieve, with the highest possible quality, nothing less than “driver-vehicle symbiosis under all conditions.” At the same time, many traffic accidents result from improper driver behavior. The author focuses on driver behavior under various driving conditions, conducting detailed measurement and analysis of visual perception and attention characteristics as well as perceptual characteristics involved in driving. The aim in doing so is to support research on driving support systems and driving workload reduction technologies that function as human-vehicle systems and take such characteristics into account. Document type: Article

Abstract

Hybrid and electric vehicles require accurate knowledge of the battery to make an educated guess about the expected electric driving range. Range prediction is complicated by the fact that batteries are subject to external influences and aging. Also the future driving behavior is often unknown. This paper presents a model-based identification method for online parameter estimation of Li-ion batteries. This allows range prediction to anticipate on all kinds of duty cycles as well as changing battery characteristics enforced by cell degradation. In the proposed methodology, parameters values of the battery model are frequently updated with the latest measurement data. Additional state observers are utilized to keep the dynamical states of the battery model up-to-date. Altogether, this offers robustness and accuracy under varying operating conditions and for the complete battery lifetime. Experimental results are presented to validate the proposed identification method. © 2011 IEEE.

Abstract

The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance. Document type: Article