Research on Classification and Recognition of Driving Styles Based on Feature Engineering

Accurate classification and the effective recognition of driving styles are critical for improving control performance of the vehicle powertrain. In this research, a set of driving style classification and recognition methods is built based on the feature engineering. First, a specified road test is conducted considering the influence factors, and meanwhile, the corresponding driving data is collected, followed by a detailed evaluation of the driving styles. Then, the information entropy is applied to discretize the driving data, including the speed, acceleration, and opening degree of the accelerator pedal, and 44 feature quantities are extracted to characterize the driving style. By analyzing strong correlation and redundancy among the constructed feature quantities, the principal component analysis (PCA) is employed to reduce the dimension, and the fuzzy c-means (FCM) clustering algorithm is leveraged to classify the driving style. The successful classification rate reaches 92.16%, which is improved by 9.81% in comparison with traditional features. Finally, a parameter identification algorithm based on the support vector machine (SVM) is applied to identify the classified driving style, and the recognition accuracy reaches 92.86%, which is improved by 7.15% in comparison with traditional features, proving the feasibility of the proposed algorithm.

[1]  Weihua Sheng,et al.  Driver drowsiness detection through HMM based dynamic modeling , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Junqiang Xi,et al.  A rapid pattern-recognition method for driving styles using clustering-based support vector machines , 2016, 2016 American Control Conference (ACC).

[3]  Michael J. Cafarella,et al.  Input selection for fast feature engineering , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[4]  Md Zahidul Islam,et al.  Discretization of continuous attributes through low frequency numerical values and attribute interdependency , 2016, Expert Syst. Appl..

[5]  A. Augustynowicz.  Preliminary classification of driving style with objective rank method , 2009 .

[6]  M. Akamatsu,et al.  Indices for characterizing driving style and their relevance to car following behavior , 2007, SICE Annual Conference 2007.

[7]  Honghai Li,et al.  Driving style estimation by fusing multiple driving behaviors: a case study of freeway in China , 2018, Cluster Computing.

[8]  Wen Jing Zhang,et al.  Driving habits analysis on vehicle data using error back-propagation neural network algorithm , 2015 .

[9]  Chao Deng,et al.  Driving style recognition method using braking characteristics based on hidden Markov model , 2017, PloS one.

[10]  Rui Xiong,et al.  Towards a smarter hybrid energy storage system based on battery and ultracapacitor - A critical review on topology and energy management , 2018, Journal of Cleaner Production.

[11]  Manohar Das,et al.  Driver Classification for Optimization of Energy Usage in a Vehicle , 2012, CSER.

[12]  Lin Li,et al.  Driving Style Classification Using a Semisupervised Support Vector Machine , 2017, IEEE Transactions on Human-Machine Systems.

[13]  Yangsheng Xu,et al.  Human Driving Behavior Recognition Based on Hidden Markov Models , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[14]  M. Mikulincer,et al.  The multidimensional driving style inventory--scale construct and validation. , 2004, Accident; analysis and prevention.

[15]  Corneliu E. Havârneanu,et al.  The Romanian Version of the Multidimensional Driving Style Inventory: Psychometric Properties and Cultural Specificities , 2015 .

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[18]  Wolfram Burgard,et al.  Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Bo Gao,et al.  Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey , 2018, IEEE Transactions on Intelligent Transportation Systems.

[20]  Hongwen He,et al.  A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[21]  Marco Brambilla,et al.  Comparison of different driving style analysis approaches based on trip segmentation over GPS information , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[22]  Yi Lu Murphey,et al.  Driver's style classification using jerk analysis , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[23]  Yi He,et al.  Curve speed model for driver assistance based on driving style classification , 2017 .

[24]  F Dai,et al.  Modelling and recognition of a driver’s starting intentions , 2012 .

[25]  Orit Taubman-Ben-Ari,et al.  The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale. , 2016, Accident; analysis and prevention.

[26]  Joonwoo Son,et al.  Relationships between Driving Style and Fuel Consumption in Highway Driving , 2011 .

[27]  Junqiang Xi,et al.  Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation , 2019 .

[28]  Lutz Eckstein,et al.  Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving , 2015 .

[29]  Junqiang Xi,et al.  Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches , 2017, IEEE Transactions on Intelligent Transportation Systems.

[30]  Bo Cheng,et al.  Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities , 2017 .

[31]  Junqiang Xi,et al.  Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey , 2014 .

[32]  Berry Eggen,et al.  Measuring driving styles: a validation of the multidimensional driving style inventory , 2015, AutomotiveUI.

[33]  Yiman Du,et al.  Leveraging longitudinal driving behaviour data with data mining techniques for driving style analysis , 2015 .