Driving Behavior Modeling Based on Hidden Markov Models with Driver's Eye-Gaze Measurement and Ego-Vehicle Localization

This paper presents a comparison of driving behavior modeling methods based on hidden Markov models (HMMs) with driver's eye-gaze measurement and ego-vehicle localization. Original HMMs are sometimes insufficient to model real-world scenarios. To overcome these limitations, extended HMMs have been proposed, e.g., autoregressive input-output HMMs (AIOHMMs). This paper first details AIOHMMs and presents ways to use them for driving behavior modeling. We compare the performance for behavior modeling and maneuver discrimination for six types of HMMs. The driving data for this work was gathered in our university campus with a car-like vehicle. Experimental results suggest that the hidden states can properly represent the average of the driving actions when the driving behaviors are accurately modeled by the HMMs. It is also suggested that surrounding and past information can be used to flexibly model the relationship between driving actions and related information.

[1]  Eijiro Takeuchi,et al.  Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[2]  Takatsugu Hirayama,et al.  Integrated modeling of driver gaze and vehicle operation behavior to estimate risk level during lane changes , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[3]  Klaus C. J. Dietmayer,et al.  Continuous Driver Intention Recognition with Hidden Markov Models , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[4]  Eijiro Takeuchi,et al.  Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[5]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[6]  Yoshua Bengio,et al.  An Input Output HMM Architecture , 1994, NIPS.

[7]  Alexander Schliep,et al.  Statistical Sensor Modelling for Autonomous Driving Using Autoregressive Input-Output HMMs , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[8]  Biing-Hwang Juang,et al.  Mixture autoregressive hidden Markov models for speech signals , 1985, IEEE Trans. Acoust. Speech Signal Process..

[9]  Meng Lu,et al.  Driver Intention Recognition Method Using Continuous Hidden Markov Model , 2011, Int. J. Comput. Intell. Syst..

[10]  Y. Kishimoto,et al.  A modeling method for predicting driving behavior concerning with driver’s past movements , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[11]  Eijiro Takeuchi,et al.  Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[12]  Mohan M. Trivedi,et al.  Looking-in and looking-out vision for Urban Intelligent Assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[13]  Paul Vernaza,et al.  r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting , 2018, ECCV.

[14]  Luke Fletcher,et al.  Correlating driver gaze with the road scene for driver assistance systems , 2005, Robotics Auton. Syst..

[15]  Hema Swetha Koppula,et al.  Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  T. Govardhan,et al.  Driver Gaze Tracking And Eyes Off The Road Detection System , 2017 .

[17]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[18]  Alexey Dosovitskiy,et al.  End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Hema Swetha Koppula,et al.  Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture , 2016, ArXiv.

[20]  Reinhard Klette,et al.  Look at the Driver, Look at the Road: No Distraction! No Accident! , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.