Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

Abstract

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

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References

Butakov

V. A.

Ioannou

Personalized Driver/Vehicle Lane Change Models for ADAS. IEEE Transactions on Vehicular Technology, Vol. 64, No. 10, 2014, pp. 4422–4431.

S.E.

Liao

Wang

Cheng

Chen

Lane Change Maneuver Recognition via Vehicle State and Driver Operation Signals—Results from Naturalistic Driving Data. 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015, pp. 865–870.

Satzoda

R. K.

Gunaratne

Trivedi

M. M.

Drive Quality Analysis of Lane Change Maneuvers for Naturalistic Driving Studies. IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015, pp. 654–659.

Morris

Doshi

Trivedi

Lane Change Intent Prediction for Driver Assistance: On-Road Design and Evaluation. IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 2011, pp. 895–901.

Lethaus

Baumann

Koster

Lemmer

Using Pattern Recognition to Predict Driver Intent. International Conference on Adaptive and Natural Computing Algorithms, Berlin, Heidelberg, 2011, pp. 140–149.

Salvucci

D. D.

Modeling Driver Behavior in a Cognitive Architecture. Human Factors, Vol. 48, No. 6, 2006, pp. 362–380.

Doshi

Trivedi

M. M.

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver’s Intent to Change Lanes. IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 2, 2009, pp. 453–462.

Hou

Jin

Niu

Sun

Driver Intention Recognition Method Using Continuous Hidden Markov Model. International Journal of Computational Intelligence Systems, Vol. 4, No. 3, 2011, pp. 386–393.

Kuge

Yamamura

Shimoyama

Liu

A Driver Behavior Recognition Method Based on a Driver Model Framework. SAE Transactions, Vol. 109, 2000, pp. 469–476.

10.

Visvikis

Smith

T. L.

Pitcher

Smith

Study on Lane Departure Warning and Lane Change Assistant Systems. Transport Research Laboratory Project Report PPR-374, Berks, 2008.

11.

Dogan

Edelbrunner

Iossifidis

Towards a Driver Model: Preliminary Study of Lane Change Behavior. 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China, 2008, pp. 931–937.

12.

Wang

Lane Changing Intention Recognition Based on Speech Recognition Models. Transportation Research Part C, Vol. 69, 2016, pp. 497–514.

13.

McCall

J. C.

Wipf

D. P.

Trivedi

M. M.

Rao

B. D.

Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 3, 2007, pp. 431–440.

14.

Mori

Takenaka

Bando

Taniguchi

Miyajima

Takeda

Automatic Lane Change Extraction Based on Temporal Patterns of Symbolized Driving Behavioral Data. IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015, pp. 976–981.

15.

Safety Pilot Model Deployment. Safety Pilot Model Deployment Data. Provided by ITS DataHub through Data.transportation.gov. http://doi.org/10.21949/1504482. 2014, Accessed March 20, 2020.

16.

Arvin

Kamrani

Khattak

A. J.

How Instantaneous Driving Behavior Contributes to Crashes at Intersections: Extracting Useful Information from Connected Vehicle Message Data. Accident Analysis and Prevention, Vol. 127, 2019, pp. 118–133.

17.

Kamrani

Arvin

Khattak

A. J.

Extracting Useful Information from Basic Safety Message Data: An Empirical Study of Driving Volatility Measures and Crash Frequency at Intersections. Transportation Research Record, Vol. 2672, No. 38, 2018, pp. 290–301.

18.

Xie

Yang

Ozbay

Yang

Use of Real-World Connected Vehicle Data in Identifying High-Risk Locations Based on a New Surrogate Safety Measure. Accident Analysis and Prevention, Vol. 125, 2019, pp. 311–319.

19.

Wang

Zhao

Hedrick

J. K.

Development and Evaluation of Two Learning-Based Personalized Driver Models for Car-Following Behaviors. American Control Conference (ACC), Seattle, WA, 2017, pp. 1133–1138.

20.

Kumar

Perrollaz

Lefevre

Laugier

Learning-Based Approach for Online Lane Change Intention Prediction. IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia, 2013, pp. 797–802.

21.

Ghaffari

Khodayari

Hosseinkhani

Salehinia

The Effect of a Lane Change on a Car-Following Maneuver: Anticipation and Relaxation Behaviour. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 229, No. 7, 2015, pp. 809–818.

22.

Yuan

Wang

Lane-Change Prediction Method for Adaptive Cruise Control System with Hidden Markov Model. Advances in Mechanical Engineering, Vol. 10, No. 9, 2018, p. 1687814018802932.

23.

Zhang

Lin

Wang

Dolan

J. M.

Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving. IEEE Transactions on Intelligent Vehicles, Vol. 3, No. 3, 2018, pp. 276–286.

24.

Zheng

Ahn

Chen

Laval

The Effects of Lane-Changing on the Immediate Follower: Anticipation, Relaxation, and Change in Driver Characteristics. Transportation Research Part C, Vol. 26, 2013, pp. 367–379.

25.

Wang

Coifman

The Effect of Lane-Change Maneuvers on a Simplified Car-Following Theory. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 3, 2008, pp. 523–535.

26.

Dogan

Edelbrunner

Iossifidis

Autonomous Driving: A Comparison of Machine Learning Techniques by Means of the Prediction of Lane Change Behavior. 2011 IEEE International Conference on Robotics and Biomimetics, Karon Beach, Thailand, 2011, pp.1837–1843.

27.

Bezzina

Sayer

Safety Pilot Model Deployment: Test Conductor Team Report. Report No. DOT-HS-812-171. U.S. Department of Transportation, Washington DC, 2015.

28.

Enclewood

Rajiwade

Safety Pilot Model Deployment: Sample Data Environment Data Handbook (Version 1.3). Research and Technology Innovation Administration, US Department of Transportation, McLean, VA, 2015.

29.

Stekhoven

D. J.

Using the missForest Package. https://stat.ethz.ch/education/semesters/ss2012/ams/paper/missForest_1.2.pdf. 2011, Accessed June 18, 2020.

30.

Zhao

Guo

Jia

Y. J.

Trafficnet: An Open Naturalistic Driving Scenario Library. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 2017, pp. 1–8.

31.

Bilmes

J. A.

A Gentle Tutorial of the EM Algorithm and Its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute, Vol. 4, No. 510, 1998, p. 126.

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