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A personalized highway driving assistance system

conference contribution
posted on 2017-07-28, 00:00 authored by S Ramyar, A Homaifar, Syed Salaken, Saeid Nahavandi, A Kurt
© 2017 IEEE. A control approach for automated highway driving is proposed in this study, which can learn from human driving data, and is applied to the longitudinal trajectory of an autonomous car. Naturalistic driving data are used as samples to train the model offline. Then, the model is used online to emulate what a human driver would do by computing acceleration. This reference acceleration is tracked by a predictive controller, which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to balance between maintaining vehicle safety and following the model's commands. Thus, the proposed controller can handle dynamic traffic situations while performing like a human driver. This approach is validated on two different scenarios using MATLAB simulations.

History

Event

Intelligent Vehicles. Symposium (2017 : Los Angeles, California)

Pagination

1596 - 1601

Publisher

IEEE

Location

Los Angeles, California

Place of publication

Piscataway, N.J.

Start date

2017-06-11

End date

2017-06-14

ISBN-13

9781509048045

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

IV 2017 : Proceedings of the IEEE Intelligent Vehicles Symposium