Design of Single-modal Take-over Request in SAE Level 2 & 3 Automated Vehicle

Recently, cutting-edge technology has led to the development of automated vehicles, but the limitations of the related technology may lead to hazardous situations. This resulted in the remarkable significance of the interaction between automated vehicles and drivers. In particular, the transition between the driver and the automated vehicle in accordance with Level 3 of SAE J3016 is inevitable, and guidelines or standards regarding the takeover should be provided. Therefore, we aim to prepare the safety guidelines for the takeover and to conduct a comparative test. First, guidelines for visual, auditory, and haptic displays in existing vehicles were examined. Second, preliminary research was conducted on the modality of automated vehicles. Third, we carried out a modality investigation regarding the partially automated vehicle. Based on this, we proposed visual, auditory, and haptic signals for each modality. This will serve as a significant starting point for future research based on multimodal methods

Enhancing driving safety and user experience through unobtrusive and function-specific feedback

Inappropriate trust in the capabilities of automated driving systems can result in misuse and insufficient monitoring behaviour that impedes safe manual driving performance following takeovers. Previous studies indicate that the communication of system uncertainty can promote appropriate use and monitoring by calibrating trust. However, existing approaches require the driver to regularly glance at the instrument cluster to perceive the changes in uncertainty. This may lead to missed uncertainty changes and user disruptions. Furthermore, the benefits of conveying the uncertainty of the different vehicle functions such as lateral and longitudinal control have yet to be explored. This research addresses these gaps by investigating the impact of unobtrusive and function-specific feedback on driving safety and user experience. Transferring knowledge from other disciplines, several different techniques will be assessed in terms of their suitability for conveying uncertainty in a driving context

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

Don't Worry, I'm in Control! Is Users’ Trust in Automated Driving Different When Using a Continuous Ambient Light HMI Compared to an Auditory HMI?

Ambient LED displays have been used to provide peripheral light-based cues to drivers about a vehicle's current state, along with providing requests for a driver's attention or action. However, few studies have investigated the use of an ambient LED display to improve drivers' trust, perceived safety, and reactions during L3 automated driving. Due to the ambient nature of an LED lightband display, it could be anticipated that it would provide reassurance of the automation status while automation is on, along with providing a gentle cue for non-urgent transitions of control. This video submission presents a methodological overview of a driving simulator study designed to evaluate the effectiveness of an ambient peripheral light display (Lightband HMI) in terms of its potential to improve drivers' trust in L3 automation, along with a comparison of a Lightband and Auditory HMI in terms of their effectiveness in facilitating transitions of control

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

Is Users’ Trust during Automated Driving Different When Using an Ambient Light HMI, Compared to an Auditory HMI?

The aim of this study was to compare the success of two different Human Machine Interfaces (HMIs) in attracting drivers’ attention when they were engaged in a Non-Driving-Related Task (NDRT) during SAE Level 3 driving. We also assessed the value of each on drivers’ perceived safety and trust. A driving simulator experiment was used to investigate drivers’ response to a non-safety-critical transition of control and five cut-in events (one hard; deceleration of 2.4 m/s2, and 4 subtle; deceleration of ~1.16 m/s2) over the course of the automated drive. The experiment used two types of HMI to trigger a takeover request (TOR): one Light-band display that flashed whenever the drivers needed to takeover control; and one auditory warning. Results showed that drivers’ levels of trust in automation were similar for both HMI conditions, in all scenarios, except during a hard cut-in event. Regarding the HMI’s capabilities to support a takeover process, the study found no differences in drivers’ takeover performance or overall gaze distribution. However, with the Light-band HMI, drivers were more likely to focus their attention to the road centre first after a takeover request. Although a high proportion of glances towards the dashboard of the vehicle was seen for both HMIs during the takeover process, the value of these ambient lighting signals for conveying automation status and takeover messages may be useful to help drivers direct their visual attention to the most suitable area after a takeover, such as the forward roadway

Whose Drive Is It Anyway? Using Multiple Sequential Drives to Establish Patterns of Learned Trust, Error Cost, and Non-Active Trust Repair While Considering Daytime and Nighttime Differences as a Proxy for Difficulty

Semi-autonomous driving is a complex task domain with a broad range of problems to consider. The human operator’s role in semi-autonomous driving is crucial because safety and performance depends on how the operator interacts with the system. Drive difficulty has not been extensively studied in automated driving systems and thus is not well understood. Additionally, few studies have studied trust development, decline, or repair over multiple drives for automated driving systems. The goal of this study was to test the effect of perceived driving difficulty on human trust in the automation and how trust is dynamically learned, reduced due to automation errors, and repaired over a seven-drive series. The experiment used 2 task difficulty conditions (easy vs. difficult) x 3 error type conditions (no error, takeover request or TOR, failure) x 7 drives mixed design. Lighting condition was used as a proxy for driving difficulty because decreased visibility for potential hazards could make monitoring the road difficult. During the experiment, 122 undergraduate participants drove an automated vehicle seven times in either a daytime (i.e., “easy”) or nighttime (i.e., “difficult”) condition. Participants experienced a critical hazard event in the fourth drive, in which the automation perfectly avoided the hazard (“no error” condition), issued a takeover request (“TOR” condition), or failed to notice and respond to the hazard (“failure” condition). Participants completed trust ratings after each drive to establish trust development. Results showed that trust improved through the first three drives, demonstrating proper trust calibration. The TOR and automation failure conditions saw significant decreases in trust after the critical hazard in drive four, whereas trust was unaffected for the no error condition. Trust naturally repaired in the TOR and failure conditions after the critical event but did not recover to previous levels before the critical event. There was no evidence of perceived difficulty differences between the daytime and nighttime conditions. Thus, a consistent lack of trust differences was found between lighting conditions. This study demonstrated how trust develops and responds to errors in automated driving systems, informing future research for trust repair interventions and design of automated driving systems

Interacting with Autonomous Vehicles: Learning from other Domains

The rise of evermore autonomy in vehicles and the expected introduction of self-driving cars have led to a focus on human interactions with such systems from an HCI perspective over the last years. Automotive User Interface researchers have been investigating issues such as transition control procedures, shared control, (over)trust, and overall user experience in automated vehicles. Now, it is time to open the research field of automated driving to other CHI research fields, such as Human-Robot-Interaction (HRI), aeronautics and space, conversational agents, or smart devices. These communities have been dealing with the interplay between humans and automated systems for more than 30 years. In this workshop, we aim to provide a forum to discuss what can be learnt from other domains for the design of autonomous vehicles. Interaction design problems that occur in these domains, such as transition control procedures, how to build trust in the system, and ethics will be discussed