%0 Conference Paper %1 6083078 %A Johnson, D.A. %A Trivedi, M.M. %B Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on %D 2011 %K activityrecognition sensors %P 1609-1615 %R 10.1109/ITSC.2011.6083078 %T Driving style recognition using a smartphone as a sensor platform %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6083078 %X Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.

@inproceedings{6083078, abstract = {Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.}, added-at = {2015-02-01T19:56:43.000+0100}, author = {Johnson, D.A. and Trivedi, M.M.}, biburl = {https://www.bibsonomy.org/bibtex/2f6358c6ef361c32f6cda6e8124358533/lutful_kabir}, booktitle = {Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on}, description = {IEEE Xplore Abstract - Driving style recognition using a smartphone as a sensor platform}, doi = {10.1109/ITSC.2011.6083078}, interhash = {5b267650005361cab703b809738e61ad}, intrahash = {f6358c6ef361c32f6cda6e8124358533}, issn = {2153-0009}, keywords = {activityrecognition sensors}, month = oct, pages = {1609-1615}, timestamp = {2015-02-01T19:56:43.000+0100}, title = {Driving style recognition using a smartphone as a sensor platform}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6083078}, year = 2011 }