Buzz or Beep? How Mode of Alert Influences Driver Takeover Following Automation Failure

abstract: Highly automated vehicles require drivers to remain aware enough to takeover during critical events. Driver distraction is a key factor that prevents drivers from reacting adequately, and thus there is need for an alert to help drivers regain situational awareness and be able to act quickly and successfully should a critical event arise. This study examines two aspects of alerts that could help facilitate driver takeover: mode (auditory and tactile) and direction (towards and away). Auditory alerts appear to be somewhat more effective than tactile alerts, though both modes produce significantly faster reaction times than no alert. Alerts moving towards the driver also appear to be more effective than alerts moving away from the driver. Future research should examine how multimodal alerts differ from single mode, and see if higher fidelity alerts influence takeover times.Dissertation/ThesisMasters Thesis Human Systems Engineering 201

Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

Systematic Review of Driver Distraction in the Context of Advanced Driver Assistance Systems (ADAS) & Automated Driving Systems (ADS)

Advanced Vehicle Systems promise improved safety and comfort for drivers. Steady advancements in technology are resulting in increasing levels of vehicle automation capabilities, furthering safety benefits. In fact, some of these vehicle automation systems are already deployed and available, but with promised benefits, such systems can potentially change driving behaviors. There is evidence that drivers have increased secondary task engagements while driving with automated vehicle systems, but there is a need for a clearer scientific understanding of any potential correlations between the use of automated vehicle systems and potentially negative driver behaviors. Therefore, this thesis aims to understand the state of knowledge on automated vehicle systems and their possible impact on drivers’ distraction behaviors. I have conducted two systematic literature reviews to examine this question. This thesis reports these reviews and examines the effects of secondary task engagement on driving behaviors such as take-over times, visual attention, trust, and workload, and discusses the implications on driver safety

자율주행 차량 제품 설명의 매체 비교 연구 : 매체에 따른 정보 전달과 신뢰 변화 차이

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2021. 2. 박우진.In this thesis, we consider how the expression of autonomous vehicle product information can be improved in terms of memory, learning workload, and trust in automation. We investigate the solution by comparing different medium types and presentation orders. As a result, we propose a design recommendation guideline that can be used in designing autonomous vehicle product education materials and assist in trust calibration.본 논문에서는 자율주행차량 제품의 정보를 사용자에게 전달하는데 있어 기억지속, 교육부하와 자동화에 대한 신뢰 측면에서 향상시킬 수 있는 방안을 고려한다. 본 연구에서는 문제에 대한 답을 찾기 위하여 다양한 매체 종류와 정보 제공 순서를 비교한다. 그 결과 본 논문에서는 자율주행차량 제품에 대한 교육을 실시하는데 있어 필요한 교자재를 어떻게 설계하고 구성해야하는지 방향을 제공하는 가이드라인을 제시하고 사용자와 자동화 시스템 사이의 적절한 신뢰 수준을 찾는데 정보를 제공한다.Chapter 1. Introduction 1 Chapter 2. Literature Review 6 2.1 Level of Autonomous Vehicle by SAE 6 2.2 Media Learning 7 2.3 Trust in Automation 7 Chapter 3. Method 9 3.1 Participants 9 3.2 Medium - Presented Instruction Format 10 3.3 Contents - Driving Scenario Situations 10 3.4 Procedure 12 3.5 Experiment Variables 14 3.6 Apparatus 16 3.7 Statistical Analysis 16 Chapter 4. Results 19 4.1 Comparison of Medium and Presentation Order in Memory 19 4.2 Recall and Retention Score Result 19 4.3 NASA-TLX Rating Result 20 4.4 Trust Rating Result 24 Chapter 5. Discussion 26 5.1 Findings and Interpretation 26 5.2 Implications 29 Chapter 6. Conclusions 32Maste

Driver Trust in Automated Driving Systems

Vehicle automation is a prominent example of safety-critical AI-based task automation. Recent digital innovations have led to the introduction of partial vehicle automation, which can already give vehicle drivers a sense of what fully automated driving would feel like. In the context of current imperfect vehicle automation, establishing an appropriate level of driver trust in automated driving systems (ADS) is seen as a key factor for their safe use and long-term acceptance. This paper thoroughly reviews and synthesizes the literature on driver trust in ADS, covering a wide range of academic disciplines. Pulling together knowledge on trustful user interaction with ADS, this paper offers a first classification of the main trust calibrators. Guided by this analysis, the paper identifies a lack of studies on adaptive, contextual trust calibration in contrast to numerous studies that focus on general trust calibration

Towards standardized metrics for measuring takeover performance in conditionally automated driving: A systematic review

A particular concern with SAE Level 3 automated vehicles is the takeover transition from the automated vehicle to the driver. Prior research has employed a wide range of metrics for measuring takeover performance. However, the lack of a set of standard metrics for measuring takeover performance makes it difficult to consolidate findings and summarize the influence of different factors. This article presents a review of the metrics employed in empirical literature examining takeover transitions in Level 3 automated driving and proposes a framework for standardizing the objective takeover performance metrics.University of Michigan McityNational Science FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168148/1/Cao et al. 2021 (DeepBlue).pdfDescription of Cao et al. 2021 (DeepBlue).pdf : Main FileSEL

Automated driving: A literature review of the take over request in conditional automation

This article belongs to the Special Issue Autonomous Vehicles TechnologyIn conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver's state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.This work was funded by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0; the Spanish Ministry of Economy, Industry and Competitiveness under the TRA201563708-R and TRA2016-78886-C3-1-R project; open access funding by the Johannes Kepler University Linz

Keeping the driver in the loop in conditionally automated driving:A perception-action theory approach

In this paper we investigated if keeping the driver in the perception–action loop during automated driving can improve take-over behavior from conditionally automated driving. To meet this aim, we designed an experiment in which visual exposure (perception) and manual control exposure (action) were manipulated. In a dynamic driving simulator experiment, participants (n = 88) performed a non-driving related task either in a head-up display in the windshield (high visual exposure) or on a head-down display near the gear shift (low visual exposure). While driving, participants were either in an intermittent control-mode with four noncritical take-over situations (high manual control exposure), or in a continuous automation-mode throughout the ride (low manual control exposure). In all conditions, a critical take-over had to be carried out after an approximately 13 min ride. Measurements of take-over behavior showed that only high visual exposure had an effect on hands-on reaction time measurements. Both visual exposure and manual control exposure had small to medium sized main effects on time to system deactivation, the maximum velocity of the steering wheel, and the standard deviation of the steering wheel angle. The combined high visual – and high manual control exposure condition led to 0.55 s faster reaction time and 37% less steering variability in comparison to the worst case low visual – and low manual control exposure condition. Together, results corroborate that maintaining visual exposure and manual control exposure during automated driving can be efficacious and suggest that their positive effects are additive