Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles

Shriram C. Jugade; Alessandro C. Victorino; Veronique B. Cherfaoui; Stratis Kanarachos

doi:10.1109/ITSC.2018.8569441

Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles

Shriram C. Jugade, Alessandro C. Victorino, Veronique B. Cherfaoui, Stratis Kanarachos

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

10 Citations (Scopus)

Abstract

Prediction of human driving decisions is an important aspect of modeling human behavior for the application to Advanced Driver Assistance Systems (ADAS) in the intelligent vehicles. This paper presents a sensor based receding horizon model for the prediction of human driving commands. Human driving decisions are expressed in terms of the vehicle speed and steering wheel angle profiles. Environmental state and human intention are the two major factors influencing the human driving decisions. The environment around the vehicle is perceived using LIDAR sensor. Feature extractor computes the occupancy grid map from the sensor data which is filtered and processed to provide precise and relevant information to the feed-forward neural network. Human intentions can be identified from the past driving decisions and represented in the form of time series data for the neural network. Supervised machine learning is used to train the neural network. Data collection and model validation is performed in the driving simulator using the SCANeR studio software. Simulation results are presented alone with the analysis.

Original language	English
Title of host publication	2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	691-696
Number of pages	6
ISBN (Electronic)	978-1-7281-0323-5
ISBN (Print)	978-1-7281-0321-1
DOIs	https://doi.org/10.1109/ITSC.2018.8569441
Publication status	Published - 10 Dec 2018
Event	21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, United States Duration: 4 Nov 2018 → 7 Nov 2018

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume	2018-November

Conference

Conference	21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/Territory	United States
City	Maui
Period	4/11/18 → 7/11/18

Funding

This project has received funding from the European Commission under the H2020 Grant agreement ITEAM No. 675999 and by LABEX MS2T, ROBOTEX 1Sorbonne universités, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, CS 60319; 60203 Compigne Cedex, France. Email: shriram.jugade/[email protected] 2Department of Mechanical Engineering, Universidade Federal de Minas Gerais (UFMG), Av Antonio Carlos 6627, Belo Horizonte, Minas Gerais, Brazil. Email: [email protected] 3Coventry University, United Kingdom. Email: [email protected]

Keywords

ADAS
Autonomous Navigation
human driving behavior
human driving decisions
Intelligent vehicle
Neural Networks
Shared Control

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Computer Science Applications

Access to Document

10.1109/ITSC.2018.8569441

Cite this

Jugade, S. C., Victorino, A. C., Cherfaoui, V. B., & Kanarachos, S. (2018). Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018 (pp. 691-696). Article 8569441 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2018.8569441

Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles. / Jugade, Shriram C.; Victorino, Alessandro C.; Cherfaoui, Veronique B. et al.
2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 691-696 8569441 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Jugade, SC, Victorino, AC, Cherfaoui, VB & Kanarachos, S 2018, Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles. in 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018., 8569441, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-November, Institute of Electrical and Electronics Engineers Inc., pp. 691-696, 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018, Maui, United States, 4/11/18. https://doi.org/10.1109/ITSC.2018.8569441

Jugade SC, Victorino AC, Cherfaoui VB, Kanarachos S. Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 691-696. 8569441. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). doi: 10.1109/ITSC.2018.8569441

Jugade, Shriram C. ; Victorino, Alessandro C. ; Cherfaoui, Veronique B. et al. / Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles. 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 691-696 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC).

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abstract = "Prediction of human driving decisions is an important aspect of modeling human behavior for the application to Advanced Driver Assistance Systems (ADAS) in the intelligent vehicles. This paper presents a sensor based receding horizon model for the prediction of human driving commands. Human driving decisions are expressed in terms of the vehicle speed and steering wheel angle profiles. Environmental state and human intention are the two major factors influencing the human driving decisions. The environment around the vehicle is perceived using LIDAR sensor. Feature extractor computes the occupancy grid map from the sensor data which is filtered and processed to provide precise and relevant information to the feed-forward neural network. Human intentions can be identified from the past driving decisions and represented in the form of time series data for the neural network. Supervised machine learning is used to train the neural network. Data collection and model validation is performed in the driving simulator using the SCANeR studio software. Simulation results are presented alone with the analysis.",

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Sensor based Prediction of Human Driving Decisions using Feed forward Neural Networks for Intelligent Vehicles

Abstract

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