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Making Overtaking Cyclists Safer: Driver Intention Models in Threat Assessment and Decision-Making of Advanced Driver Assistance System.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Making Overtaking Cyclists Safer: Driver Intention Models in Threat Assessment and Decision-Making of Advanced Driver Assistance System./
Author:	Thalya, Prateek.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	36 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
Contained By:	Dissertations Abstracts International83-06B.
Subject:	Software. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28829896
ISBN:	9798496571517

Making Overtaking Cyclists Safer: Driver Intention Models in Threat Assessment and Decision-Making of Advanced Driver Assistance System.
Thalya, Prateek.

Making Overtaking Cyclists Safer: Driver Intention Models in Threat Assessment and Decision-Making of Advanced Driver Assistance System. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 36 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (Lic. Eng)--Chalmers Tekniska Hogskola (Sweden), 2021.

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

Introduction:The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS).Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS's performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present.Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system.Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system's effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment programs.

ISBN: 9798496571517Subjects--Topical Terms:

619355
Software.

Making Overtaking Cyclists Safer: Driver Intention Models in Threat Assessment and Decision-Making of Advanced Driver Assistance System.
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502 $a Thesis (Lic. Eng)--Chalmers Tekniska Hogskola (Sweden), 2021.
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520 $a Introduction:The number of cyclist fatalities makes up 3% of all fatalities globally and 7.8% in the European Union. Cars overtaking cyclists on rural roads are complex situations. Miscommunication and misunderstandings between road users may lead to crashes and severe injuries, particularly to cyclists, due to lack of protection. When making a car overtaking a cyclist safer, it is important to understand the interaction between road users and use in the development of an Advanced Driver Assistance System (ADAS).Methods: First, a literature review was carried out on driver and interaction modeling. A Unified Modeling Language (UML) framework was introduced to operationalize the interaction definition to be used in the development of ADAS. Second, the threat assessment and decision-making algorithm were developed that included the driver intention model. The counterfactual simulation was carried out on artificial crash data and field data to understand the intention-based ADAS's performance and crash avoidance compared to a conventional system. The method focused on cars overtaking cyclists when an oncoming vehicle was present.Results: An operationalized definition of interaction was proposed to highlight the interaction between road users. The framework proposed uses UML diagrams to include interaction in the existing driver modeling approaches. The intention-based ADAS results showed that using the intention model, earlier warning or emergency braking intervention can be activated to avoid a potential rear-end collision with a cyclist without increasing more false activations than a conventional system.Conclusion: The approach used to integrate the driver intention model in developing an intention-based ADAS can improve the system's effectiveness without compromising its acceptance. The intention-based ADAS has implications towards reducing worldwide road fatalities and in achieving sustainable development goals and car assessment programs.
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