Automation and the situation awareness of drivers in agricultural semi-autonomous vehicles

2014 ◽  
Vol 124 ◽  
pp. 8-15 ◽  
Author(s):  
Behzad Bashiri ◽  
Danny D. Mann
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles

scholarly journals Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

Safety ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 34
Author(s):  
Shi Cao ◽  
Pinyan Tang ◽  
Xu Sun
Keyword(s):  
Reaction Time ◽  
Interior Design ◽  
Situation Awareness ◽  
Repeated Measures ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Maximum Magnitude ◽  
Significant Difference ◽  
Driving Conditions

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

scholarly journals Perception Through Image Processing Applications for Situation Awareness

2020 ◽  
Vol 7 (2) ◽  
pp. 13-16
Author(s):  
Julkar Nine ◽  
Shanmugapriyan Manoharan ◽  
Wolfram Hardt
Keyword(s):  
Image Processing ◽  
Expert System ◽  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Raspberry Pi ◽  
Multiple Image ◽  
Custom Hardware ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Camera Sensors

This paper introduces a concept for the perception level of situation awareness through image processing applications. The perception of the information from the environment is lacking to achieve highly autonomous vehicles. This approach mainly focuses on collecting information from the environment using camera sensors. The frames from the camera are processed using multiple image processing algorithms, outputs converted into CAN message, and sent to the expert system. CE-Box custom hardware that consists of a Raspberry Pi 3b model counted with PiCAN 2 used to implement and evaluate this approach. The goal of this paper is to provide a conceptual method to make decisions based on the extracted information from the environment using image processing algorithms.

Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S

2017 ◽  
Vol 11 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Mica R. Endsley
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Model Development ◽  
Autonomous Driving ◽  
Discrete Control ◽  
Driving Safety ◽  
Future Research ◽  
Continuous Control ◽  
Development Mode

Autonomous and semiautonomous vehicles are currently being developed by over14 companies. These vehicles may improve driving safety and convenience, or they may create new challenges for drivers, particularly with regard to situation awareness (SA) and autonomy interaction. I conducted a naturalistic driving study on the autonomy features in the Tesla Model S, recording my experiences over a 6-month period, including assessments of SA and problems with the autonomy. This preliminary analysis provides insights into the challenges that drivers may face in dealing with new autonomous automobiles in realistic driving conditions, and it extends previous research on human-autonomy interaction to the driving domain. Issues were found with driver training, mental model development, mode confusion, unexpected mode interactions, SA, and susceptibility to distraction. New insights into challenges with semiautonomous driving systems include increased variability in SA, the replacement of continuous control with serial discrete control, and the need for more complex decisions. Issues that deserve consideration in future research and a set of guidelines for driver interfaces of autonomous systems are presented and used to create recommendations for improving driver SA when interacting with autonomous vehicles.

scholarly journals Updating our understanding of situation awareness in relation to remote operators of autonomous vehicles

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Clare Mutzenich ◽  
Szonya Durant ◽  
Shaun Helman ◽  
Polly Dalton
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Automated Driving ◽  
Industry Standard ◽  
Remote Operation ◽  
Regulatory Frameworks ◽  
Human Driver ◽  
Driver Error ◽  
To Come ◽  
Temporary Control

AbstractThe introduction of autonomous vehicles (AVs) could prevent many accidents attributable to human driver error. However, even entirely driverless vehicles will sometimes require remote human intervention. Current taxonomies of automated driving do not acknowledge the possibility of remote control of AVs or the challenges that are unique to such a driver in charge of a vehicle that they are not physically occupying. Yet there are significant differences between situation awareness (SA) in normal driving contexts and SA in these remote driving operations. We argue that the established understanding of automated driving requires updating to include the context of remote operation that is likely to come in to play at higher levels of automation. It is imperative to integrate the role of the remote operator within industry standard taxonomies, so that regulatory frameworks can be established with regards to the training required for remote operation, the necessary equipment and technology, and a comprehensive inventory of the use cases under which we could expect remote operation to be carried out. We emphasise the importance of designing control interfaces in a way that will maximise remote operator (RO) SA and we identify some principles for designing systems aimed at increasing an RO’s sense of embodiment in the AV that requires temporary control.

Situation awareness in remote operators of autonomous vehicles: Developing a taxonomy of situation awareness in video-relays of driving scenes

2021 ◽  
Author(s):  
clare mutzenich ◽  
Szonya Durant ◽  
Shaun Helman ◽  
Polly Dalton
Keyword(s):  
Human Factors ◽  
Situation Awareness ◽  
Mental Model ◽  
Autonomous Vehicles ◽  
Human Intervention ◽  
Novel Approach ◽  
Video Feed ◽  
Remote Environment ◽  
Verbal Data ◽  
Remote Driving

Even entirely driverless vehicles will sometimes require remote human intervention. Existing SA frameworks do not acknowledge the significant human factors challenges unique to a driver in charge of a vehicle that they are not physically occupying. Remote operators will have to build up a mental model of the remote environment facilitated by monitor view and video feed. We took a novel approach to 'freeze and probe' techniques to measure SA, employing a qualitative verbal elicitation task to uncover what people ‘see’ in a remote scene when they are not constrained by rigid questioning. Participants (n=10) watched eight videos of driving scenes randomised and counterbalanced across four road types (motorway, rural, residential and A road). Participants recorded spoken descriptions when each video stopped, detailing what was happening (comprehension) and what could happen next (prediction). Participant transcripts provided a rich catalogue of verbal data reflecting clear interactions between different SA levels. This suggests that acquiring SA in remote scenes is a flexible and fluctuating process of combining comprehension and prediction globally rather than serially, in contrast to what has sometimes been implied by previous SA methodologies (Endsley, 2000; Endsley, 2017; Jones &amp; Endsley, 1996). Participants’ responses were also categorised to form a ‘Taxonomy of SA’ aimed at capturing the key elements of people’s reported SA for videos of driving situations. We suggest that existing theories of SA need to be more sensitively applied to remote driving contexts such as remote operators of autonomous vehicles.

“Driver Take Over”: A Preliminary Exploration of Driver Trust and Performance in Autonomous Vehicles

2017 ◽  
Vol 61 (1) ◽  
pp. 1969-1973 ◽  
Author(s):  
Michelle Hester ◽  
Kevin Lee ◽  
Brian P. Dyre
Keyword(s):  
Situation Awareness ◽  
Autonomous Vehicles ◽  
Autonomous Systems ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Preliminary Study ◽  
And Performance ◽  
Task Irrelevant ◽  
Statistical Results ◽  
And Task

Automated vehicles are becoming more prominent in research and development. These automated vehicles introduce issues that have been seen in other autonomous systems such as decreases in situation awareness, complacency, and trust. Previous literature has looked at the effects of alerts and voice agents on driving performance. This preliminary study compares different in-car alerts (no alert, sound alert, task irrelevant voice alert, and task relevant voice alert) on trust and the driver’s ability to get back in-the-loop when the automation has failed. Participants were asked to monitor a simulated automated vehicle as it drove down a straight two-lane road. The main statistical results of our study show no difference in trust between the four different conditions; however, more participants avoided collision with a leading car in the task relevant voice condition in comparison to the three other conditions. These preliminary findings have important implications for the design of automated vehicles.

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