Driver Vigilance in Automated Vehicles: Investigating Hazard Detection Performance

2017 ◽  
Vol 61 (1) ◽  
pp. 1369-1369
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Detection Performance ◽  
Automated Vehicles ◽  
Hazard Detection

Driver Vigilance in Automated Vehicles: Effects of Demands on Hazard Detection Performance

2018 ◽  
Vol 61 (3) ◽  
pp. 474-487 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Detection Performance ◽  
Task Demands ◽  
Spatial Uncertainty ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Hazard Detection ◽  
High Workload ◽  
And Performance ◽  
The Impact

Objective: The current study investigated driver vigilance in partially automated vehicles to determine whether increased task demands reduce a driver’s ability to monitor for automation failures and whether the vigilance decrement associated with hazard detections is due to driver overload. Background: Drivers of partially automated vehicles are expected to monitor for signs of automation failure. Previous research has shown that a driver’s ability to perform this duty declines over time. One possible explanation for this vigilance decrement is that the extreme demands of vigilance causes overload and leads to depletion of limited attentional resources required for vigilance. Method: Participants completed a 40-min drive in a simulated partially automated vehicle and were tasked with monitoring for hazards that represented potential automation failures. Two factors were manipulated to test the impact of monitoring demands on performance: Spatial uncertainty and event rate. Results: As predicted, hazard detection performance was poorer when monitoring demands were increased, and performance declined as a function of time on task. Subjective reports also indicated high workload and task-induced stress. Conclusion: Drivers of partially automated vehicles are impaired by the vigilance decrement and elevated task demands, meaning that safe operation becomes less likely when the demands associated with monitoring automation increase and as a drive extends in duration. This study also supports the notion that vigilance performance in partially automated vehicles is likely due to driver overload. Application: Developers of automation technologies should consider countermeasures that attenuate a driver’s cognitive load when tasked with monitoring automation.

Driver Vigilance in Automated Vehicles: Hazard Detection Failures Are a Matter of Time

2018 ◽  
Vol 60 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Eric T. Greenlee ◽  
Patricia R. DeLucia ◽  
David C. Newton
Keyword(s):  
Safety Concern ◽  
Reaction Times ◽  
Vigilance Task ◽  
Time On Task ◽  
Vigilance Decrement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Hazard Detection ◽  
And Performance

Objective: The primary aim of the current study was to determine whether monitoring the roadway for hazards during automated driving results in a vigilance decrement. Background: Although automated vehicles are relatively novel, the nature of human-automation interaction within them has the classic hallmarks of a vigilance task. Drivers must maintain attention for prolonged periods of time to detect and respond to rare and unpredictable events, for example, roadway hazards that automation may be ill equipped to detect. Given the similarity with traditional vigilance tasks, we predicted that drivers of a simulated automated vehicle would demonstrate a vigilance decrement in hazard detection performance. Method: Participants “drove” a simulated automated vehicle for 40 minutes. During that time, their task was to monitor the roadway for roadway hazards. Results: As predicted, hazard detection rate declined precipitously, and reaction times slowed as the drive progressed. Further, subjective ratings of workload and task-related stress indicated that sustained monitoring is demanding and distressing and it is a challenge to maintain task engagement. Conclusion: Monitoring the roadway for potential hazards during automated driving results in workload, stress, and performance decrements similar to those observed in traditional vigilance tasks. Application: To the degree that vigilance is required of automated vehicle drivers, performance errors and associated safety risks are likely to occur as a function of time on task. Vigilance should be a focal safety concern in the development of vehicle automation.

scholarly journals Identification and Explanation of Challenging Conditions for Camera-Based Object Detection of Automated Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3699 ◽  
Author(s):  
Thomas Ponn ◽  
Thomas Kröger ◽  
Frank Diermeyer
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Influence Factors ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Automated Vehicles ◽  
Detection Algorithms ◽  
Type Approval ◽  
Market Launch ◽  
First Time

For a safe market launch of automated vehicles, the risks of the overall system as well as the sub-components must be efficiently identified and evaluated. This also includes camera-based object detection using artificial intelligence algorithms. It is trivial and explainable that due to the principle of the camera, performance depends highly on the environmental conditions and can be poor, for example in heavy fog. However, there are other factors influencing the performance of camera-based object detection, which will be comprehensively investigated for the first time in this paper. Furthermore, a precise modeling of the detection performance and the explanation of individual detection results is not possible due to the artificial intelligence based algorithms used. Therefore, a modeling approach based on the investigated influence factors is proposed and the newly developed SHapley Additive exPlanations (SHAP) approach is adopted to analyze and explain the detection performance of different object detection algorithms. The results show that many influence factors such as the relative rotation of an object towards the camera or the position of an object on the image have basically the same influence on the detection performance regardless of the detection algorithm used. In particular, the revealed weaknesses of the tested object detectors can be used to derive challenging and critical scenarios for the testing and type approval of automated vehicles.

Improvement of Handheld Radar Operators’ Hazard Detection Performance Using 3-D Visualization

2017 ◽  
Vol 14 (11) ◽  
pp. 1888-1892
Author(s):  
Drew B. Gonsalves ◽  
Lawrence H. Winner ◽  
Joseph N. Wilson
Keyword(s):  
Detection Performance ◽  
Hazard Detection

Effects of spatial training on hazard detection with simulated head up displays

2010 ◽  
Author(s):  
Heather M. Mong ◽  
Nicole Murchison ◽  
Benjamin A. Clegg
Keyword(s):  
Hazard Detection ◽  
Spatial Training

Automated Vehicles Questionnaire

2018 ◽  
Author(s):  
Timo Liljamo ◽  
Heikki Liimatainen ◽  
Markus Pöllänen
Keyword(s):  
Automated Vehicles

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Sign in / Sign up

Export Citation Format

Share Document