WhozDriving: Abnormal driving trajectory detection by studying multi-faceted driving behavior features

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9784, Issue , 2016, Pages 135-144

  He, Meng ^a   Guo, Bin ^a   Chen, Huihui ^a   Chin, Alvin ^b   Tian, Jilei ^b   Yu, Zhiwen ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^b BMW Manufacturing Co   (United States)

Author keywords

Anomaly detection; Driver behavior patterns; Driver identification; GPS trajectory; KNN

Indexed keywords

BEHAVIORAL RESEARCH; SIGNAL DETECTION; TRAJECTORIES;

ANOMALY DETECTION; CONTEXT-BASED RECOMMENDATIONS; DRIVER BEHAVIOR; DRIVER IDENTIFICATION; DRIVING BEHAVIOR; GPS TRAJECTORIES; MISCLASSIFICATIONS; SUPERVISED LEARNING METHODS;

BIG DATA;

EID: 84979074025     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-42553-5_12     Document Type: Conference Paper

References (16)

1. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)
2. Lühr, S., West, G., Venkatesh, S.: Recognition of emergent human behaviour in a smart home: a data mining approach. Pervasive Mob. Comput. 3(2), 95–116 (2007)
3. Candás, J.L.C., Peláez, V., López, G., et al.: An automatic data mining method to detect abnormal human behaviour using physical activity measurements. Pervasive Mob. Comput. 15, 228–241 (2014)
4. Guo, B., Zhang, D., Yu, Z., Liang, Y., Wang, Z., Zhou, X.: From the internet of things to embedded intelligence. World Wide Web J. (WWWJ) 16(4), 399–420 (2013)
5. Chikhaoui, B., Wang, S., Pigot, H.: ADR-SPLDA: activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation. Pervasive Mob. Comput. 8(6), 845–862 (2012)
6. Wang, L., Gu, T., Tao, X., Chen, H., Lu, J.: Recognizing multi-user activities using wearable sensors in a smart home. Pervasive Mob. Comput. 7(3), 287–298 (2011)
7. Chen, C., Cook, D., Crandall, A.: The user side of sustainability: modeling behavior and energy usage in the home. Pervasive Mob. Comput. 9(1), 161–175 (2013)
8. Khan, W.A., Hussain, M., Afzal, M., Amin, M.B., Lee, S.: Healthcare standards based sensory data exchange for home healthcare monitoring system. In: Engineering in Medicine and Biology Society (EMBC), pp. 1274–1277 (2012)
9. Virone, G.: Assessing everyday life behavioral rhythms for the older generation. Pervasive Mob. Comput. 5(5), 606–622 (2009)
10. Koh, D.W., Kang, H.B.: Smartphone-based modeling and detection of aggressiveness reactions in senior drivers. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 12–17. IEEE (2015)
11. Jensen, M., Wagner, J., Alexander, K.: Analysis of in-vehicle driver behaviour data for improved safety. Int. J. Veh. Saf. 5(3), 197–212 (2011)
12. Rigolli, M., Williams, Q., Gooding, M.J., et al.: Driver behavioural classification from trajectory data. In: Proceedings of the 2005 IEEE Intelligent Transportation Systems, pp. 889–894. IEEE (2005)
13. Mascaro, S., Nicholso, A.E., Korb, K.B.: Anomaly detection in vessel tracks using Bayesian networks. Int. J. Approximate Reasoning 55(1), 84–98 (2014)
14. Muniyandi, A.P., Rajeswari, R., Rajaram, R.: Network anomaly detection by cascading k-means clustering and C4. 5 decision tree algorithm. Procedia Eng. 30, 174–182 (2012)
15. Amer, M., Goldstein, M.: Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer. In: Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012), pp. 1–12 (2012)
16. Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2112–2119. IEEE (2012)