TY - GEN
T1 - Stay As You Were
T2 - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
AU - Park, Kyung Ho
AU - Park, Eunji
AU - Kim, Huy Kang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/19
Y1 - 2021/9/19
N2 - Driver behavior profiling is a significant technology in intelligent transportation as it provides contextual knowledge regarding the driver's aggressiveness. The prior studies analyzed the data's temporal characteristics and established classifiers between the normal and aggressive driver behavior in a supervised manner. However, there exist limits that the practitioner should acquire a labeled dataset, and the model could not identify unseen driver behaviors a priori. To hedge the aforementioned limits, our study proposes a novel driver behavior profiling approach under the normality discovery paradigm, which is unsupervised learning. First, we presented practical feature engineering steps to transform the smartphone IMU's raw sensor measurements to the sequence of driving data. Second, we established an unsupervised driver profiling approach that necessitates the driving data of normal driver behavior only for the model training. Third, we figured out each aggressive driver behavior has a different sequence length to represent its unique patterns. Lastly, we compared our approach's performance with a supervised approach and resulted in our unsupervised model achieved similar performance in identifying aggressive right turn, left turn, and left lane change, but required further improvements in recognizing an aggressive left lane change, aggressive braking, and aggressive acceleration.
AB - Driver behavior profiling is a significant technology in intelligent transportation as it provides contextual knowledge regarding the driver's aggressiveness. The prior studies analyzed the data's temporal characteristics and established classifiers between the normal and aggressive driver behavior in a supervised manner. However, there exist limits that the practitioner should acquire a labeled dataset, and the model could not identify unseen driver behaviors a priori. To hedge the aforementioned limits, our study proposes a novel driver behavior profiling approach under the normality discovery paradigm, which is unsupervised learning. First, we presented practical feature engineering steps to transform the smartphone IMU's raw sensor measurements to the sequence of driving data. Second, we established an unsupervised driver profiling approach that necessitates the driving data of normal driver behavior only for the model training. Third, we figured out each aggressive driver behavior has a different sequence length to represent its unique patterns. Lastly, we compared our approach's performance with a supervised approach and resulted in our unsupervised model achieved similar performance in identifying aggressive right turn, left turn, and left lane change, but required further improvements in recognizing an aggressive left lane change, aggressive braking, and aggressive acceleration.
UR - http://www.scopus.com/inward/record.url?scp=85118437973&partnerID=8YFLogxK
U2 - 10.1109/ITSC48978.2021.9564814
DO - 10.1109/ITSC48978.2021.9564814
M3 - Conference contribution
AN - SCOPUS:85118437973
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 278
EP - 284
BT - 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 September 2021 through 22 September 2021
ER -