{"title":"Improvement of the road traffic management by an ant-hierarchical fuzzy system","authors":"H. M. Kammoun, I. Kallel, A. Alimi, J. Casillas","doi":"10.1109/CIVTS.2011.5949535","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949535","url":null,"abstract":"In view of dynamicity on road networks and the sharp increase of traffic jam states, the road traffic management becomes more complex. It is clear that the shortest path algorithm based only on road length is no longer relevant.We propose in this paper a hybrid method based on two stages based on ant colony behavior and hierarchical fuzzy system. This method allows adjusting intelligently and promptly the road traffic according to the real-time changes in the road network states by the integration of an adaptive vehicle guidance system. The proposed method is implemented as a deliberative module of a vehicle ant agent in a collaborative multiagent system representing the entire road network. Series of simulations, under a multiagent platform, allow us to discuss the improvement of the global road traffic quality in terms of time, fluidity, and adaptability.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116130909","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":"Overview of CI research in automotive ITS","authors":"D. Prokhorov","doi":"10.1109/CIVTS.2011.5949538","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949538","url":null,"abstract":"Illustrative examples of applications of the CI methods in ITS are overviewed. They include off-board and on-board applications. On-board object recognition is further discussed from the standpoint of making a more reliable system for ITS applications.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127651302","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":"Grid-based visual terrain classification for outdoor robots using local features","authors":"Yasir Niaz Khan, P. Komma, K. Bohlmann, A. Zell","doi":"10.1109/CIVTS.2011.5949534","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949534","url":null,"abstract":"In this paper we present a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. We compare the more traditional texture classification approaches, such as Local Binary Patterns, Local Ternary Patterns and a newer extension Local Adaptive Ternary Patterns, and also modify and test three non-traditional approaches called SURF, DAISY and CCH. We drove our robot under different weather and ground conditions and captured images of five different terrain types for our experiments. We did not filter out blurred images which are due to robot motion and other artifacts caused by rain, etc.We used Random Forests for classification, and cross-validation for the verification of our results. The results show that most of the approaches work well for terrain classification in a fast moving mobile robot, despite image blur and other artifacts induced due to extremely variant weather conditions.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129725237","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}
Dimitar Filev, F. Tseng, Johannes Kristinsson, R. McGee
{"title":"Contextual on-board learning and prediction of vehicle destinations","authors":"Dimitar Filev, F. Tseng, Johannes Kristinsson, R. McGee","doi":"10.1109/CIVTS.2011.5949539","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949539","url":null,"abstract":"This paper deals with the problem of on-board learning of typical stop locations and the prediction of the vehicle destination. Such a learning and prediction procedure is used to summarize the stop locations, estimate the frequent destinations, and learn the driver's decision model of selecting the next destinations under different conditions. The prediction of the driver's usage pattern is useful in generating optimal control policies for energy management control in electrified vehicles. The proposed approach is based on the real-time clustering and learning of a decision model that combines fuzzy and Markov models. The former is applied to represent possibilistically the context of the destination selection while the latter covers the probabilistic process of choosing a destination for given conditions.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124888218","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}
Luka Eciolaza, G. Triviño, Beatriz Delgado, J. Rojas, M. Sevillano
{"title":"Fuzzy linguistic reporting in driving simulators","authors":"Luka Eciolaza, G. Triviño, Beatriz Delgado, J. Rojas, M. Sevillano","doi":"10.1109/CIVTS.2011.5949529","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949529","url":null,"abstract":"The growth of new intelligent transportation on-board systems has increased dramatically drivers' attention to secondary tasks other than driving. Potential risk of committing mistakes while driving suggests the need to evaluate the onboard systems in order to ensure safe driving practices.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"413 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122783485","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}
L. Re, Markus Hirsch, D. Alberer, Stephan M. Winkler
{"title":"The role of data choice in data driven identification for online emission models","authors":"L. Re, Markus Hirsch, D. Alberer, Stephan M. Winkler","doi":"10.1109/CIVTS.2011.5949537","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949537","url":null,"abstract":"Data driven models are known to be a valid alternative to first principle approaches for modeling. However, in the case of complex and largely unknown systems such as the chemical reactions leading to engine emissions, experience shows that results from data driven models suffer from a significant dependence on the actual data set used for identification and are prone to an excessive complexity. This paper shows how the use of an incremental design of experiments based on polynomial models can be used to determine the appropriate complexity of the data set as well as a suitable measurement profile which yields an adequate excitation for the model parameter estimation. As this paper shows experimentally, this result is not specific to the particular identification approach used, but the same data set can be used e.g. by genetic programming (GP) algorithms which extract also the model structure from data. Results are shown using emission measurements on a modern turbocharged Diesel engine on an emission test bench.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"36 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129704927","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":"Batch reinforcement learning for optimizing longitudinal driving assistance strategies","authors":"O. Pietquin, F. Tango, R. Aras","doi":"10.1109/CIVTS.2011.5949533","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949533","url":null,"abstract":"Partially Autonomous Driver's Assistance Systems (PADAS) are systems aiming at providing a safer driving experience to people. Especially, one application of such systems is to assist the drivers in reacting optimally so as to prevent collisions with a leading vehicle. Several means can be used by a PADAS to reach this goal. For instance, warning signals can be sent to the driver or the PADAS can actually modify the speed of the car by braking automatically. An optimal combination of different warning signals together with assistive braking is expected to reduce the probability of collision. How to associate the right combination of PADAS actions to a given situation so as to achieve this aim remains an open problem. In this paper, the use of a statistical machine learning method, namely the reinforcement learning paradigm, is proposed to automatically derive an optimal PADAS action selection strategy from a database of driving experiments. Experimental results conducted on actual car simulators with human drivers show that this method achieves a significant reduction of the risk of collision.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124392570","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":"Design of a toy submarine using underwater vehicle design optimization framework","authors":"Khairul Alam, T. Ray, S. Anavatti","doi":"10.1109/CIVTS.2011.5949527","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949527","url":null,"abstract":"This paper presents a framework for optimum design of a small, low-cost, light-weight toy submarine for recreational purposes. Two state of the art optimization algorithms namely Non-dominated sorting genetic algorithm (NSGA-II) and Infeasibility driven evolutionary algorithm (IDEA) have been used in this study to carry out optimization of the toy submarine design. Single objective formulation of the toy submarine design problem is considered in this paper to identify designs with minimum drag and internal clash free assembly. The flexibility of the proposed framework and its ability to identify optimum preliminary designs of a toy submarine are demonstrated. Design identified through the process of optimization is compared with an existing toy submarine to highlight the benefits offered by the present approach.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129155228","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":"A system for the traffic control in signposted junctions","authors":"S. Elkosantini, Salima Mnif, H. Chabchoub","doi":"10.1109/CIVTS.2011.5949528","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949528","url":null,"abstract":"The traffic congestion has become a serious problem in a city. The traffic congestion had important consequences in terms of social, economic and environmental preoccupations. For this reason, several ITS was proposed and their role is to manage the existing highway, public transportation and railroad infrastructure to ease congestion and respond to crises. Developer of such system seek to have a system that insure a safer and more convenient travel for people. In this paper, we propose a system for junctions traffic lights control based on case based reasoning (CBR) approach and fuzzy sets theory. In fact, the CBR is always considered as a cyclic paradigm of Artificial Intelligence and that is used to learning and problem solving based on past experience. The developed system is tested with on a virtual junction and the obtained results are discussed.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129162988","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":"Context-based estimation of driver intent at road intersections","authors":"S. Lefèvre, J. Guzman, C. Laugier","doi":"10.1109/CIVTS.2011.5949532","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949532","url":null,"abstract":"Navigating through a road intersection is a complex manoeuvre that requires understanding the spatio-temporal relationships that exist between vehicles. Situation understanding and prediction are therefore fundamental functions for any computer-controlled safety or navigation system applied to road intersections. To interpret the situation at an intersection it is necessary to infer the intended manoeuvre of the relevant vehicles. Conventional approaches to manoeuvre prediction rely mainly on vehicle kinematics and dynamics. The contention of this paper is that contextual information in the form of topological and geometrical characteristics of the intersection can provide useful cues to understand the behaviour of a vehicle. We describe a probabilistic framework that extracts information from a digital map and uses it along with vehicle state information to estimate a driver's intended manoeuvre. The proposed approach is applicable to different types of intersections and handles uncertainty on the input information. We evaluate the performance of our approach on several real life scenarios using data recorded from real traffic.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129203219","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}