{"title":"Fault diagnosis of in-wheel BLDC motor drive for electric vehicle application","authors":"A. Tashakori, M. Ektesabi","doi":"10.1109/IVS.2013.6629585","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629585","url":null,"abstract":"Permanent magnet Brushless DC (BLDC) motors have been attracted by electric vehicle (EV) manufacturers in the last decade. The paper presents a simple fault diagnosis technique to detect switch faults of three phases Voltage Source Inverter (VSI) drive of BLDC motor in a closed-loop control scheme. The proposed fault diagnosis system is capable to detect the fault occurrence, identify fault type and the faulty switch of inverter based on Discrete Fourier Transform (DFT) analysis of the measured line voltages of BLDC motor. BLDC motor drive and the proposed fault diagnosis system are simulated. Simulation results were validated first by experimental data for BLDC motor operation under healthy condition. A knowledge based table is developed to identify switch faults of VSI by analyzing the simulation results under various fault conditions. The proposed fault diagnosis algorithm does not need massive computational effort and can be implemented as a subroutine with a closed-loop control algorithm of the BLDC motor on a single chip microcontroller. The obtained results show correct detection and identification of inverter switch faults in BLDC motor.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132209473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Autonomous vehicle social behavior for highway entrance ramp management","authors":"Junqing Wei, J. Dolan, B. Litkouhi","doi":"10.1109/IVS.2013.6629471","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629471","url":null,"abstract":"“Socially cooperative driving” is an integral part of our everyday driving, hence requiring special attention to imbue the autonomous driving with a more natural driving behavior. In this paper, an intention-integrated Prediction- and Cost function-Based algorithm (iPCB) framework is proposed to enable an autonomous vehicle to perform cooperative social behavior. An intention estimator is developed to extract the probability of surrounding agents' intentions in real time. Then for each candidate strategy, a prediction engine considering the interaction between host and surrounding agents is used to predict future scenarios. A cost function-based evaluation is applied to compute the cost for each scenario and select the decision corresponding to the lowest cost. The algorithm was tested in simulation on an autonomous vehicle cooperating with vehicles merging from freeway entrance ramps with 10,000 randomly generated scenarios. Compared with approaches that do not take social behavior into account, the iPCB algorithm shows a 41.7% performance improvement based on the chosen cost functions.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132677470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of hierarchical Bayesian estimation to calibrating a car-following model with time-varying parameters","authors":"Makoto Kasai, S. Shibagaki, S. Terabe","doi":"10.1109/IVS.2013.6629576","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629576","url":null,"abstract":"The problem of congestion caused by capacity bottleneck phenomena in access-controlled road sections should be addressed. A description of the relation between car-following behavior and vertical gradient is expected to contribute to the development of effective measures, including accurate parameter tuning of adaptive cruise control systems. This paper develops a methodology for revealing this relation. First, a model with time-varying parameters allows the characteristics of the car-following behavior to be expressed depending on the vertical gradient. Second, to account for the gradual change in vertical gradient in considering car-following behavior, a hierarchical Bayesian model is applied to the description of gradual change. Third, Markov chain Monte Carlo method is implemented as a technique for finding a solution. An example of estimation is presented to demonstrate the procedure. Conclusions suggest future directions for extending this study to devising measures for mitigating congestion on expressways.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116289300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Road user tracking at intersections using a multiple-model PHD filter","authors":"D. Meissner, Stephan Reuter, K. Dietmayer","doi":"10.1109/IVS.2013.6629498","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629498","url":null,"abstract":"A major aim of the joint project Ko-PER is the mitigation of fatal accidents at urban intersections. Therefore several test intersections have been equipped with multiple laser range finders to recognize and track road users. Besides a high traffic density the variety of road users is challenging. In this contribution a multiple-model (MM) probability hypothesis density filter with a track representation extended by class probabilities is proposed. The approach enables tracking of road users with appropriate motion models using a single MM filter. Due to the estimation of the class probabilities an adaption of the transition probabilities between the models is possible. The performance of the road user tracking is evaluated using real world data.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"99 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Khan, Verónica Suaste, Diego Caudillo, R. Klette
{"title":"Belief Propagation stereo matching compared to iSGM on binocular or trinocular video data","authors":"W. Khan, Verónica Suaste, Diego Caudillo, R. Klette","doi":"10.1109/IVS.2013.6629563","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629563","url":null,"abstract":"The report about the ECCV 2012 Robust Vision Challenge summarized strengths and weaknesses of the winning stereo matcher (Iterative Semi-Global Matching = iSGM). In this paper we discuss whether two variants of a Belief Propagation (BP) matcher can cope better with events such as sun flare or missing temporal consistency, where iSGM showed weaknesses (similar to the original SGM). The two variants are defined by the use of a census data cost function on a 5 × 5 window and either a linear or a quadratic truncated smoothness function. An evaluation on data with ground truth showed better results for the linear smoothness function. The BP matcher with the linear smoothness function provided then also better matching results (compared to iSGM) on some of the test sequences (e.g. images with sun flare). The third-eye approach was used for the performance evaluation.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132162925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modeling unstructured environments with dynamic persistence grids and object delimiters in urban traffic scenarios","authors":"A. Vatavu, S. Nedevschi","doi":"10.1109/IVS.2013.6629518","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629518","url":null,"abstract":"Modeling dynamic environments is an essential research topic in any driving assistance system. The complexity of the surrounding world, the measurement uncertainties or the unpredictable behavior of the traffic participants are the main factors that influence the detection and tracking process. In this paper we present a vision-based method for modeling and tracking unstructured dynamic environments. The proposed solution relies on raw information provided by a classified grid computed from a digital elevation map and employs two separate representation levels: a local dynamic persistence grid (DyPerGrid) that is generated as an intermediate representation level and a map of delimiters as a higher level obstacle description. A fast tracking solution is proposed by using the two models. The result is a geometrically consistent and accurate representation of the dynamic environment.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niko Sünderhauf, Marcus Obst, Sven Lange, G. Wanielik, P. Protzel
{"title":"Switchable constraints and incremental smoothing for online mitigation of non-line-of-sight and multipath effects","authors":"Niko Sünderhauf, Marcus Obst, Sven Lange, G. Wanielik, P. Protzel","doi":"10.1109/IVS.2013.6629480","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629480","url":null,"abstract":"Reliable vehicle positioning is a crucial requirement for many applications of advanced driver assistance systems. While satellite navigation provides a reasonable performance in general, it often suffers from multipath and non-line-of-sight errors when it is applied in urban areas and therefore does not guarantee consistent results anymore. Our paper proposes a novel online method that identifies and excludes the affected pseudorange measurements. Our approach does not depend on additional sensors, maps, or environmental models. We rather formulate the positioning problem as a Bayesian inference problem in a factor graph and combine the recently developed concept of switchable constraints with an algorithm for efficient incremental inference in such graphs. We furthermore introduce the concepts of auxiliary updates and factor graph pruning in order to accelerate convergence while keeping the graph size and required runtime bounded. A real-world experiment demonstrates that the resulting algorithm is able to successfully localize despite a large number of satellite observations are influenced by NLOS or multipath effects.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133042915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"MCMC particle filter-based vehicle tracking method using multiple hypotheses and appearance model","authors":"Y. Lim, Dongyoung Kim, Chung-Hee Lee","doi":"10.1109/IVS.2013.6629618","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629618","url":null,"abstract":"In this study, we propose a multiple vehicle tracking method using multiple hypotheses and the appearance model. The multiple hypotheses are associated with multiple tracks using track-to-multiple hypotheses association method. A target state is estimated using the maximum a posteriori probability estimation method. The posterior probability is proportional to the product of a priori probability and the likelihood that is calculated using similarities of multiple hypotheses and the appearance model. The posterior probability density function is estimated using the Markov chain Monte Carlo particle filter. An optimal posterior target state is determined using a sample with the maximum a posteriori probability. Our experimental results show that the proposed method can improve multiple objects tracking precision as well as multiple object tracking accuracy.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134530278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Stereo accuracy for collision avoidance for varying collision trajectories","authors":"W. Khan, R. Klette","doi":"10.1109/IVS.2013.6629639","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629639","url":null,"abstract":"In this study, we have generalized our previous tool for assisting a safety engineer in assessing collision trajectories by extending from colliding objects with constant velocity to more general variable velocity ones. We have also highlighted that a linear system cannot be relied upon for handling a colliding object with variable velocity. To deal with such trajectories, past observations are weighted depending on velocities at those locations; priority is given to locations with reduced velocity. Based on this hypothesis, we have shown that the weighted system outperforms a linear one. The benefit is that it always issues a timely warning, even if the trajectory of the colliding object keeps on changing over time.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132988695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visual ego-vehicle lane assignment using Spatial Ray features","authors":"Tobias Kühnl, F. Kummert, J. Fritsch","doi":"10.1109/IVS.2013.6629613","DOIUrl":"https://doi.org/10.1109/IVS.2013.6629613","url":null,"abstract":"Assigning the ego-vehicle to a lane is not only beneficial for navigation but will be an essential element in future Advanced Driver Assistance Systems. This paper describes an approach for ego-lane index estimation using only a monocular camera and no additional sensing equipment like, e.g., the typically employed GPS and Inertial Measurement Unit. Key aspect of the approach are SPatial RAY (SPRAY) features which represent the spatial layout of the road in the visual scene. The proposed method perceives a variety of local visual properties of the scene by means of base classifiers operating on patches extracted from camera images. The spatial arrangement of these local visual properties are captured using SPRAY features. With a boosting classifier trained on these features the ego-lane index is obtained. The system is evaluated on low traffic density and complementary to an object-based approach suitable for heavy traffic. In the conducted experiments, the proposed approach reaches recognition rates of 93% to 97% on individual highway images without applying any kind of temporal filtering.","PeriodicalId":251198,"journal":{"name":"2013 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129318354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}