http://iet.metastore.ingenta.com
1887

Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation

Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Driving style recognition plays a crucial role in eco-driving, road safety, and intelligent vehicle control. This study proposes a statistical-based recognition method to deal with driver behaviour uncertainty in driving style recognition. First, the authors extract discriminative features using the conditional kernel density function to characterise path-following behaviour. Meanwhile, the posterior probability of each selected feature is computed based on the full Bayesian theory. Second, they develop an efficient Euclidean distance-based method to recognise the path-following style for new input datasets at a low computational cost. By comparing the Euclidean distance of each pair of elements in the feature vector, then they classify driving styles into seven levels from normal to aggressive. Finally, they employ a cross-validation method to evaluate the utility of their proposed approach by comparing with a fuzzy logic (FL) method. The experiment results show that the proposed statistical-based recognition method integrating with the kernel density is more efficient and robust than the FL method.

References

    1. 1)
      • 1. Zhou, M., Jin, H., Wang, W.: ‘A review of vehicle fuel consumption models to evaluate eco-driving and eco-routing’, Transp. Res. D, Transp. Environ., 2016, 49, pp. 203218.
    2. 2)
      • 2. Martinez, C.M., Heucke, M., Wang, F., et al: ‘Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey’, IEEE Trans. Intell. Transp. Syst., 2017, doi: 10.1109/TITS.2017.2706978.
    3. 3)
      • 3. Li, Y., Wang, J., Chan, C.-Y., et al: ‘Develop right-turn real-time crash warning system at arterial access considering driver behaviour’, IET Intell. Transp. Syst., 2017, 11, (1), pp. 4452.
    4. 4)
      • 4. Li, L., Liu, Y., Wang, J., et al: ‘Human dynamics based driver model for autonomous car’, IET Intell. Transp. Syst., 2016, 10, (8), pp. 545554.
    5. 5)
      • 5. Wang, J., Wang, J., Wang, R., et al: ‘A framework of vehicle trajectory replanning in lane exchanging with considerations of driver characteristics’, IEEE Trans. Veh. Technol., 2017, 66, (5), pp. 35833596.
    6. 6)
      • 6. Schnelle, S., Wang, J., Su, H., et al: ‘A driver steering model with personalized desired path generation’, IEEE Trans. Syst. Man Cybern., Syst., 2017, 47, (1), pp. 111120.
    7. 7)
      • 7. Sagberg, F., Giulio, S., Piccinini, F.B., et al: ‘A review of research on driving styles and road safety’, Hum. Factors, 2015, 57, (7), pp. 12481275.
    8. 8)
      • 8. Themann, P., Bock, J., Eckstein, L.: ‘Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving’, IET Intell. Transp. Syst., 2015, 9, (1), pp. 5058.
    9. 9)
      • 9. Wang, W., Xi, J.: ‘Study of semi-active suspension control strategy based on driving behaviour characteristics’, Int. J. Veh. Des., 2015, 68, (1-3), pp. 141161.
    10. 10)
      • 10. Wang, W., Xi, J., Liu, C., et al: ‘Human-centered feed-forward control of a vehicle steering system based on a driver's path-following characteristics’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (6), pp. 14401453.
    11. 11)
      • 11. Wang, W., Xi, J., Chen, H.: ‘Modelling and recognizing driver behavior based on driving data: a survey’, Math. Probl. Eng., 2014, 2014, p. 20.
    12. 12)
      • 12. Candamo, J., Shreve, M., Goldgof, D.B., et al: ‘Understanding transit scenes: A survey on human behavior-recognition algorithms’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (1), pp. 206224.
    13. 13)
      • 13. Tadesse, E., Sheng, W., Liu, M.: ‘Driver drowsiness detection through HMM based dynamic modeling’. IEEE Int. Conf. Robotics & Automation, Hong Kong, China, 2014, pp. 40034008.
    14. 14)
      • 14. Gadepally, V., Krishnamurthy, A., Özgüner, Ü.: ‘A framework for estimating driver decisions near intersections’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (2), pp. 637646.
    15. 15)
      • 15. Akita, T., Inagaki, S., Suzuki, T., et al: ‘Hybrid system modeling of human driver in the vehicle following task’. SICE Annual Conf., Kagawa University, Japan, 2007, pp. 11221127.
    16. 16)
      • 16. Sundbom, M., Falcone, P., Sjöberg, J.: ‘Online driver behavior classification using probabilistic ARX models’. The 16th Int. IEEE Annual Conf. Intelligent Transportation Systems, Hague, Netherlands, 2013, pp. 11071112.
    17. 17)
      • 17. Sekizawa, S., Inagaki, S., Suzuki, T., et al: ‘Modeling and recognition of driving behavior based on stochastic switched ARX model’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (4), pp. 593606.
    18. 18)
      • 18. Shi, B., Xu, L., Jiang, H., et al: ‘Comparing fuel consumption based on normalised driving behaviour: a case study on major cities in China’, IET Intell. Transp. Syst., 2017, 11, (4), pp. 189195.
    19. 19)
      • 19. Angkititrakul, P., Terashima, R., Wakita, T.: ‘On the use of stochastic driver behavior model in lane departure warning’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (1), pp. 174183.
    20. 20)
      • 20. Wang, W., Zhao, D.: ‘Evaluation of lane departure correction systems using a regenerative stochastic driver model’, IEEE Trans. Intell. Veh., 2017, 2, (3), pp. 221232.
    21. 21)
      • 21. Zhang, Y., Lin, W.C., Chin, Y.S.: ‘A pattern-recognition approach for driving skill characterization’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (4), pp. 905916.
    22. 22)
      • 22. Higgs, B., Abbas, M.: ‘Segmentation and clustering of car-following behaviour: recognition of driving patterns’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (1), pp. 8190.
    23. 23)
      • 23. Qi, G., Du, Y., Wu, J., et al: ‘Leveraging longitudinal driving behaviour data with data mining techniques for driving style analysis’, IET Intell. Transp. Syst., 2015, 9, (8), pp. 792801.
    24. 24)
      • 24. Wang, W., Xi, J., Zhao, D.: ‘Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches’. 2017, arXiv:1703.09744.
    25. 25)
      • 25. Chu, D., Deng, Z., He, Y., et al: ‘Curve speed model for driver assistance based on driving style classification’, IET Intell. Transp. Syst., 2017, 11, (8), pp. 501510.
    26. 26)
      • 26. Schubert, B.: ‘Evaluating the utility of driving: toward automated decision making under uncertainty’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (1), pp. 354364.
    27. 27)
      • 27. Khushaba, R.N., Kodagoda, S., Lal, S., et al: ‘Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm’, IEEE Trans. Biomed. Eng., 2011, 58, (1), pp. 121131.
    28. 28)
      • 28. Klir, G.J.: ‘Uncertainty and information: foundations of generalized information theory’ (John Wiley & Sons, New York, 2005).
    29. 29)
      • 29. Quintero, M.C.G., López, J.O., Pinilla, A.C.C.: ‘Driver behaviour classification model based on an intelligent driving diagnosis system’. IEEE Int. Conf. Intelligent Transportation Systems, Anchorage, AK, 2012, pp. 894899.
    30. 30)
      • 30. Higgs, B., Abbas, M.: ‘A two-step segmentation algorithm for behavioural clustering of naturalistic driving styles’. IEEE Annual Conf. Intelligent Transportation Systems, Hague, Netherlands, 2013, pp. 857862.
    31. 31)
      • 31. Kasper, D., Weidl, G., Dang, T.: ‘Object-oriented Bayesian networks for detection of lane change maneuvers’, Intell. Transp. Mag., 2012, 4, (1), pp. 110.
    32. 32)
      • 32. Healey, J.A., Picard, R.W.: ‘Detecting stress during real-world driving tasks using physiological sensors’, IEEE Trans. Intell. Transp. Syst., 2005, 6, (2), pp. 156166.
    33. 33)
      • 33. Miyajima, C., Nishiwaki, Y., Ozawa, K., et al: ‘Driver modeling based on driving behavior and its evaluation in driver identification’, Proc. IEEE, 2007, 95, (2), pp. 427437.
    34. 34)
      • 34. Wang, W., Xi, J., Chong, A., et al: ‘Driving style classification using a semi-supervised support vector machine’, IEEE Trans. Human-Mach. Syst., 2017, 47, (5), pp. 650660.
    35. 35)
      • 35. Richard, C.M., Campbell, J.L., Lichty, M.G., et al: ‘Motivations for speeding’. Volume I: Summary report, Report No. DOT HS 811 658, National Highway Traffic Safety Administration, Washington, DC, 2012.
    36. 36)
      • 36. Hülnhagen, T., Dengler, I., Tamke, A., et al: ‘Maneuver recognition using probabilistic finite-state machines and fuzzy logic’. IEEE Intelligent Vehicles Symp., San Diego, CA, USA, 2010, pp. 6570.
    37. 37)
      • 37. Wahab, A., Quek, C., Tan, C.K., et al: ‘Driving profile modeling and recognition based on soft computing approach’, IEEE Trans. Neural Netw., 2009, 20, (4), pp. 563582.
    38. 38)
      • 38. Duda, R.O., Hart, P.E., Stork, D.G.: ‘Pattern classification’, 2nd, 2000.
    39. 39)
      • 39. Wang, W., Liu, C., Zhao, D.: ‘How much data are enough? a statistical approach with case study on longitudinal driving behavior’, IEEE Trans. Intell. Veh., 2017, 2, (2), pp. 8598.
    40. 40)
      • 40. Wang, W., Xi, J., Wang, J.: ‘Human-centred feed-forward control of a vehicle steering system based on a driver's steering model’. IEEE American Control Conf., Chicago, IL, USA, 2015, pp. 33613366.
    41. 41)
      • 41. Xi, J., Zong, Y., Wang, W.: ‘Research on virtual experimental teaching platform of vehicle electronic control’, Lab. Res. Explor., 2015, 34, (4), pp. 7983.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0379
Loading

Related content

content/journals/10.1049/iet-its.2017.0379
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address