상세정보
Export to Refworks부가기능
Effect of Instructions & Vehicle Control Algorithms on Driver Behavior
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Effect of Instructions & Vehicle Control Algorithms on Driver Behavior. |
| 개인저자 | Price, Morgan. |
| 단체저자명 | The University of Wisconsin - Madison. Industrial Engineering. |
| 발행사항 | [S.l.]: The University of Wisconsin - Madison., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 113 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781687939036 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Wisconsin - Madison, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Lee, John. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Background: Highly automated vehicles are changing the role of the driver. The Society of Automotive Engineers (SAE) has defined the roles of the driver and the automation in terms of levels of automation. Vehicles with Level 2 automation control the speed and steering of the vehicle and drivers are primarily responsible for driving. Vehicles with Level 3 automation control the speed and steering, but the automation is primarily responsible. The subtle differences between levels of automation could result in driver confusion. Instructions to the driver about their level of responsibility paired with feedback in the form of vehicle control algorithms could help mitigate driver confusion.Method: Two simulator experiments were conducted. In both experiments, instructions specified whether the driver or the automation was primarily responsible for driving. Experiment 1 investigated lane-centering, lane-keeping, and state-based adaptive algorithms. Experiment 2 investigated lane-centering and event-based adaptive algorithms. The state-based adaptive algorithm changed the vehicle's behavior based on changes in the driver's state. The event-based adaptive algorithm changed the vehicle's behavior based on changes in the roadway environment.Results: Both experiments found a three-way interaction between algorithm, level of responsibility and traffic demand. Independently the effect of instructions and algorithms followed a similar pattern but had different strengths of effect. Experiment 1 found the state-based adaptive algorithm had no detectable effect while instructions affected attention to the road. Experiment 2 found the event-based adaptive algorithm affected attention to the road while instructions had no detectable effect. A secondary data analysis found that drivers have faster reaction times to the state-based adaptive algorithm but maintain attention to the road longer when experiencing the event-based adaptive algorithm. Latent hazards and the event-based adaptive algorithms affected drivers' attention to the road and automation use, while instructions to the driver did not. Findings suggest the strength of the vehicle control algorithm's signal influenced driver behavior, resulting in the event-based adaptive algorithm having a stronger effect on driver behavior.Conclusions: Instructions are insufficient and may not be enough to help drivers build appropriate expectations of automation. Adaptive vehicle control algorithms are a promising source of feedback, but they are imperfect and may need further real-time feedback to augment the information they provide. |
| 일반주제명 | Transportation. Industrial engineering. Behavioral psychology. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
