{"title":"Conceptual design of a driving habit recognition framework","authors":"Dante Papada, K. Jablokow","doi":"10.1109/CIVTS.2011.5949531","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949531","url":null,"abstract":"All drivers operate vehicles differently and demonstrate varying habits behind the wheel. Some drivers may execute vehicle maneuvers more cautiously than others, and some drivers may operate the vehicle with extreme inefficiencies. The habits developed by drivers can be viewed as a sequence or pattern of events that uniquely define the habitual behavior of the vehicle operator. In this paper, a conceptual design of a recognition system is discussed to classify sequences or patterns in vehicle data extracted from the Engine Control Unit in order to provide information about the vehicle operator's driving habits. Through an application of accepted pattern recognition techniques, Fuzzy Adaptive Resonance Theory, and Modern Control System Theory, a conceptual system framework was realized. To complement the conceptual design relationships between certain vehicle data parameters and certain human behaviors, models were developed to demonstrate these relationships created by this conceptual framework. These relationships were categorized and simulated in terms of vehicle safety and efficiency. Variables or factors were chosen to develop driving habit behavior models, such as wheel slippage, vehicle braking, fuel efficiency, and base or vehicle efficiency. The new conceptual framework was successfully validated through MATLAB simulations, consisting of 4 behavior models with a range of 11 variants. Evaluations of these behaviors provided the necessary feedback, via direct mapping of vehicle data points to a continuum of behavior types, to improve the vehicle operator's decision making.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"139 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114047883","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 redundancy-based approach for visual navigation with collision avoidance","authors":"A. Cherubini, F. Spindler, F. Chaumette","doi":"10.1109/CIVTS.2011.5949530","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949530","url":null,"abstract":"We propose an autonomous vehicle guidance framework which combines visual navigation with simultaneous obstacle avoidance. The method was originally designed in [1], but real outdoor experiments and major improvements have been added in this paper. Kinematic redundancy guarantees that obstacle avoidance and navigation are achieved concurrently. The two tasks are realized both in an obstacle-free and in a dangerous context, and the control law is smoothened in between. The experiments show that with our method, the vehicle can replay a taught visual path while avoiding collisions.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"25 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":"126618524","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}
Ruoqian Liu, Shen Xu, Jungme Park, Y. Murphey, Johannes Kristinsson, R. McGee, M. Kuang, Tony Phillips
{"title":"Real time vehicle speed predition using gas-kinetic traffic modeling","authors":"Ruoqian Liu, Shen Xu, Jungme Park, Y. Murphey, Johannes Kristinsson, R. McGee, M. Kuang, Tony Phillips","doi":"10.1109/CIVTS.2011.5949536","DOIUrl":"https://doi.org/10.1109/CIVTS.2011.5949536","url":null,"abstract":"Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, macroscopic and kinetic traffic modeling approaches are investigated. We present a speed prediction algorithm, KTM-SP, based on gas-kinetic traffic modeling. Experimental results show that the proposed algorithm gave good prediction results on real traffic data.","PeriodicalId":312839,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS) Proceedings","volume":"82 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":"124701284","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}