Access Type

Open Access Dissertation

Date of Award

January 2017

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

First Advisor

Abhilash Pandya

Abstract

For automated driving vehicles to be accepted by their users and safely integrate with traffic involving human drivers, they need to act and behave like human drivers. This not only involves understanding how the human driver or occupant in the automated vehicle expects their vehicle to operate, but also involves how other road users perceive the automated vehicle’s intentions. This research aimed at learning how drivers space themselves while driving around other vehicles. It is shown that an optimized lane change maneuver does create a solution that is much different than what a human would do. There is a need to learn complex driving preferences from studying human drivers.

This research fills the gap in terms of learning human driving styles by providing an example of learned behavior (vehicle spacing) and the needed framework for encapsulating the learned data. A complete framework from problem formulation to data gathering and learning from human driving data was formulated as part of this research. On-road vehicle data were gathered while a human driver drove a vehicle. The driver was asked to make lane changes for stationary vehicles in his path with various road curvature conditions and speeds. The gathered data, as well as Learning from Demonstration techniques, were used in formulating the spacing profile as a lane change maneuver. A concise feature set from captured data was identified to strongly represent a driver’s spacing profile and a model was developed. The learned model represented the driver’s spacing profile from stationary vehicles within acceptable statistical tolerance. This work provides a methodology for many other scenarios from which human-like driving style and related parameters can be learned and applied to automated vehicles

Download

Share

COinS