Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide metho...Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.展开更多
Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by manyindustries including insurance and vehicle rental companies. The main goal of this paper is to provid...Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by manyindustries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to model the quality ofthe driving behaviour based on FIS （fuzzy inference systems）. The models consider vehicle dynamics as long as the human behaviourparameters, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition,assessment-driving behaviour model is simulated and tested by two different FISs： Mamdani and Sugeno-TSK. The simulation resultsillustrate the critical distinctions between the two FISs using the proposed driving behaviour models. These differences are based onvarious processing times, robust behaviour of the FISs, outputs MFs （membership functions）, fuzzification-techniques, flexibility inthe systems design and computational efficiency.展开更多
文摘Continuous vehicle tracking as well as detecting accidents, are significant services that are needed by many industries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to detect the position of car accident. The models consider GPS/INS-based navigation algorithm, calibration of navigational sensors, a de-nosing method as long as vehicle accident, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition, the location-based accident detection model is tested in different scenarios. The results illustrate that under harsh environments with no GPS signal, location of accident can be detected. Also results confirm that calibration of sensors has an important role in position correction algorithm. Finally, the results present that the proposed accident detection algorithm can recognize accidents and related its positions.
文摘Continuous vehicle tracking as well as monitoring driving behaviour, is significant services that are needed by manyindustries including insurance and vehicle rental companies. The main goal of this paper is to provide methods to model the quality ofthe driving behaviour based on FIS （fuzzy inference systems）. The models consider vehicle dynamics as long as the human behaviourparameters, expressed by a set of raw measurements which are obtained from various environmental sensors. In addition,assessment-driving behaviour model is simulated and tested by two different FISs： Mamdani and Sugeno-TSK. The simulation resultsillustrate the critical distinctions between the two FISs using the proposed driving behaviour models. These differences are based onvarious processing times, robust behaviour of the FISs, outputs MFs （membership functions）, fuzzification-techniques, flexibility inthe systems design and computational efficiency.
