Leveraging longitudinal driving behaviour data with data mining techniques for driving style analysis

Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand different driving styles, this study employs the clustering method and topic model to extract latent driving states, which can elaborate and analyse the commonness and individuality of driving behaviour characteristics with the longitudinal driving behaviour data collected by the instrumented vehicle. To handle the large set of data and discover the valuable knowledge, the data mining techniques including ensemble clustering method based on the kernel fuzzy C-means algorithm and the modified latent Dirichlet allocation model are employed in this study. The 'aggressive', 'cautious' and 'moderate' driving states are discovered and the underlying quantified structure is built for the driving style analysis. Language: en

[1]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  Artur Rygula.  Driving Style Identification Method Based on Speed Graph Analysis , 2009, 2009 International Conference on Biometrics and Kansei Engineering.

[4]  Nuno Vasconcelos,et al.  Latent Dirichlet Allocation Models for Image Classification , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  S Stradling,et al.  Errors and violations on the roads: a real distinction? , 1990, Ergonomics.

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  D. Gazis,et al.  Nonlinear Follow-the-Leader Models of Traffic Flow , 1961 .

[8]  Nasser Kehtarnavaz,et al.  A transportable neural-network approach to autonomous vehicle following , 1998 .

[9]  Aimin Yang,et al.  A KFCM-Based Fuzzy Classifier , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[10]  Marco Calabrese,et al.  NN-based measurements for driving pattern classification , 2009, 2009 IEEE Instrumentation and Measurement Technology Conference.

[11]  Monica Vladoiu,et al.  Driving Style Analysis Using Data Mining Techniques , 2010, Int. J. Comput. Commun. Control.

[12]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[13]  W Leutzbach,et al.  DEVELOPMENT AND APPLICATIONS OF TRAFFIC SIMULATION MODELS AT THE KARLSRUHE INSTITUT FUR VERKEHRWESEN , 1986 .

[14]  Mike McDonald,et al.  Fuzzy sets and systems for a motorway microscopic simulation model , 2000, Fuzzy Sets Syst..

[15]  Mark A. Girolami,et al.  Employing Latent Dirichlet Allocation for fraud detection in telecommunications , 2007, Pattern Recognit. Lett..

[16]  R. B. Potts,et al.  Car-Following Theory of Steady-State Traffic Flow , 1959 .

[17]  Tomer Toledo,et al.  Driving Behaviour: Models and Challenges , 2007 .

[18]  Mihai Datcu,et al.  Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation , 2010, IEEE Geoscience and Remote Sensing Letters.

[19]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[20]  Younghoon Kim,et al.  TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation , 2014, Inf. Syst..

[21]  Adrian F. M. Smith,et al.  Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus , 1993 .

[22]  R. Jiang,et al.  Full velocity difference model for a car-following theory. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  E. Montroll,et al.  Traffic Dynamics: Studies in Car Following , 1958 .

[24]  Letha H. Etzkorn,et al.  Bug localization using latent Dirichlet allocation , 2010, Inf. Softw. Technol..

[25]  Tsuna Sasaki,et al.  A Safety Index for Traffic with Linear Spacing , 1959 .

[26]  Z Bareket,et al.  USING NEURAL NETWORKS TO IDENTIFY DRIVING STYLE AND HEADWAY CONTROL BEHAVIOR OF DRIVERS , 1998 .

[27]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[28]  D. French,et al.  Behavioral correlates of individual differences in road-traffic crash risk: an examination method and findings. , 1993, Psychological bulletin.

[29]  Ahmad Aljaafreh,et al.  Driving style recognition using fuzzy logic , 2012, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012).