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Adaptive Behavioral Planning for More Social Automated Vehicles.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Adaptive Behavioral Planning for More Social Automated Vehicles./
Author:	Noonan, T. Zachary.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	113 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: A.
Contained By:	Dissertations Abstracts International83-02A.
Subject:	Industrial engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28410527
ISBN:	9798534660425

Adaptive Behavioral Planning for More Social Automated Vehicles.
Noonan, T. Zachary.

Adaptive Behavioral Planning for More Social Automated Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 113 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: A.

Thesis (Ph.D.)--The University of Iowa, 2021.

This item must not be sold to any third party vendors.

Driving automation technology is progressing at a rapid pace, and highly automated vehicles promise to be a fixture on public roadways soon. An open area of research in this domain involves how to design the behavior of highly automated vehicles best to assimilate and comingle with human drivers. The solution to this problem involves research on a broad swath of topics, including human perception and cognition, vehicle sensing technologies, social interaction, and computer science. This thesis takes a human factors-based approach to solving this problem.The theoretical basis of the model hinges on the idea that driving behavior is limited by the avoidance of visual cues relating to the perception of risk. A control theoretic model for this risk-perception-based model was proposed and validated against a naturalistic dataset of driving trajectories. The model converged to all test cases and was accurate to within the tolerance of vehicle sensing technologies in 91.7% of test cases.A novel model of automated driving decision-making was then proposed using the previously described model. This model is unique in that it does not estimate the strategic value of the other vehicle's actions, but instead estimates the behavioral limitations of the other drivers based on observed risk-perception parameters. This model was found to provide statistically significant improvement for the automated vehicle compared to control cases where the model was not present.Finally, the impact of the proposed model on the traffic characteristics was measured based on the induced trajectories of the subject vehicle and other vehicle. The proposed model was found to have a significant impact on traffic flow and throughput, measures of traffic performance that represent impacts on systemic traffic characteristics.This work could have an impact on the way automated vehicles designed for mixed-traffic environments are built. The model-based approach means that a smaller volume of data is required to train the model, which allows for a high level of adaptability. Additionally, this approach means that the parameters utilized by the model are highly interpretable, which could aid in wide acceptance of this method. Ultimately this thesis represents a first step in a new approach to designing automated driving systems that are more able to negotiate the complex social interactions requisite in the driving task.

ISBN: 9798534660425Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Automated driving

Adaptive Behavioral Planning for More Social Automated Vehicles.
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