Situation Awareness in Automated Vehicles through Proximal Peripheral Light Signals

2017 ◽  
Author(s):  
Tom van Veen ◽  
Juffrizal Karjanto ◽  
Jacques Terken
Keyword(s):  
Situation Awareness ◽  
Automated Vehicles ◽  
Light Signals

Hazard Analysis of Action Loops for Automated Vehicle Remote Operation

2021 ◽  
Vol 65 (1) ◽  
pp. 732-736
Author(s):  
Amudha V. Kamaraj ◽  
Joshua E. Domeyer ◽  
John D. Lee
Keyword(s):  
Human Factors ◽  
Situation Awareness ◽  
Process Analysis ◽  
Hazard Analysis ◽  
System Level ◽  
Automated Vehicles ◽  
Control Loops ◽  
Remote Operation ◽  
Multiple Vehicles ◽  
Human Operators

One way to compensate for the limitations of automated vehicles is to use a remote operator as a fallback controller. Indeed, this has been proposed for fleet management and intermittent vehicle control. However, existing remote operation applications have demonstrated control challenges, such as latency and bandwidth, that inhibit the effectiveness of human operators. Additionally, human factors challenges arising due to the roles of multiple remote operators managing multiple vehicles further complicates these interventions. This paper uses the Systems Theoretic Process Analysis hazard analysis technique to identify system-level issues related to the remote operation of automated vehicles. Human factors challenges are identified through the lens of two control loops that link remote drivers, dispatchers, and vehicle automation. These control loops reveal familiar challenges, such as situation awareness and mental model mismatches, as well as novel challenges, such as poorly synchronized and misaligned control.

Novel Auditory Displays in Highly Automated Vehicles: Sonification Improves Driver Situation Awareness, Perceived Workload, and Overall Experience

2021 ◽  
Vol 65 (1) ◽  
pp. 586-590
Author(s):  
Chihab Nadri ◽  
Sangjin Ko ◽  
Colin Diggs ◽  
Michael Winters ◽  
V. K. Sreehari ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Visual Display ◽  
Use Cases ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Auditory Displays ◽  
User Perceptions ◽  
Young Drivers ◽  
Group Interviews

Highly automated driving systems are expected to require the design of new user-vehicle interactions. Sonification can be used to provide contextualized alarms and cues that can increase situation awareness and user experience. In this study, we examined user perceptions of potential use cases for level 4 automated vehicles in online focus group interviews (N=12). Also, in a driving simulator study, we evaluated (1) visual-only display; (2) non-speech with visual display; and (3) speech with visual display with 20 young drivers. Results indicated participants’ interest in the use cases and insight on desired functions in highly automated vehicles. Both audiovisual display conditions resulted in higher situation awareness for drivers than the visual-only condition. Some differences were found between the non-speech and speech conditions suggesting benefits of sonification for both driving and non-driving related auditory use cases. This study will provide guidance on sonification design for highly automated vehicles.

Peripheral Monitoring of Traffic in Conditionally Automated Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1828-1832 ◽  
Author(s):  
Jonas Radlmayr ◽  
Karin Brüch ◽  
Kathrin Schmidt ◽  
Christine Solbeck ◽  
Tristan Wehner
Keyword(s):  
Situation Awareness ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Active System ◽  
Subject Design ◽  
Level 3 ◽  
Driving Task

Conditionally automated vehicles (level 3) allow drivers to engage in visual, non-driving related tasks (NDRTs) while the automation is active. System limits require drivers to reengage in the dynamic driving task in take-over situations. If the NDRT is visually engaging, situation awareness (SA) necessary for a successful take-over can decrease. This study analyzed, if the SA of drivers increases while monitoring the surrounding traffic peripherally. A semi-transparent balloon game in the head-up display operationalized the engagement into a visual NDRT with the possibility of peripheral monitoring. In addition, participants without the possibility of monitoring due to simulated heavy fog (second group) were tested along with a third group that could monitor surroundings self-determined without a NDRT. The between-subject design included 57 participants. Results showed that self-determined monitoring leads to higher situation awareness compared to peripheral monitoring and no monitoring. This did not result in better take-over performances.

Personalised neural networks for a driver intention prediction: communication as enabler for automated driving

2020 ◽  
Vol 9 (6) ◽  
pp. 357-364
Author(s):  
Johannes Reschke ◽  
Cornelius Neumann ◽  
Stephan Berlitz
Keyword(s):  
Neural Networks ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Positive Rate ◽  
Extensive Introduction ◽  
Light Signals ◽  
Set Up ◽  
Almost All

AbstractIn everyday traffic, pedestrians rely on informal communication with other road users. In case of automated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization procedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic, especially the FPR has to be improved considerably.

The Relationship Between Takeover Request Lead Time and Drivers’ Situation Awareness for Freeway Exiting in Conditionally Automated Driving

2021 ◽  
Vol 65 (1) ◽  
pp. 1385-1389
Author(s):  
Xiaomei Tan ◽  
Yiqi Zhang
Keyword(s):  
Situation Awareness ◽  
Lead Time ◽  
Manual Control ◽  
Lead Times ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Web Based ◽  
Positive Effects ◽  
Out Of The Loop ◽  
The Relationship

Conditionally automated vehicles require the out-of-the-loop driver to intervene when the system is unable to handle forthcoming situations, such as freeway exiting. The takeover request (ToR) for exiting a freeway can be scheduled in advance. Upon a ToR, the driver needs to gain situation awareness (SA) and resume manual control. This study examined how the ToR lead time affects driver SA for resuming control and when to send the ToR is most appropriate for freeway exiting. A web-based, supervised experiment was conducted with 31 participants. Each participant experienced 12 levels of ToR lead time (6, 8, 10, 12, 14, 16, 18, 20, 25, 30, 45, and 60 s). The results showed positive effects of longer ToR lead times (16–60 s) on driver SA for resuming control to exit from freeways in comparison to shorter ToR lead times (6–14 s), and the effects level off at 16–30 s.

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