The Effect of Cognitive Load on Auditory Susceptibility During Automated Driving

Objective We experimentally test the effect of cognitive load on auditory susceptibility during automated driving. Background In automated vehicles, auditory alerts are frequently used to request human intervention. To ensure safe operation, human drivers need to be susceptible to auditory information. Previous work found reduced susceptibility during manual driving and in a lesser amount during automated driving. However, in practice, drivers also perform nondriving tasks during automated driving, of which the associated cognitive load may further reduce susceptibility to auditory information. We therefore study the effect of cognitive load during automated driving on auditory susceptibility. Method Twenty-four participants were driven in a simulated automated car. Concurrently, they performed a task with two levels of cognitive load: repeat a noun or generate a verb that expresses the use of this noun. Every noun was followed by a probe stimulus to elicit a neurophysiological response: the frontal P3 (fP3), which is a known indicator for the level of auditory susceptibility. Results The fP3 was significantly lower during automated driving with cognitive load compared with without. The difficulty level of the cognitive task (repeat or generate) showed no effect. Conclusion Engaging in other tasks during automated driving decreases auditory susceptibility as indicated by a reduced fP3. Application Nondriving task can create additional cognitive load. Our study shows that performing such tasks during automated driving reduces the susceptibility for auditory alerts. This can inform designers of semi-automated vehicles (SAE levels 3 and 4), where human intervention might be needed.

Download Full-text

Ethics of Artificial Intelligence in Transport

The Oxford Handbook of Ethics of AI ◽

10.1093/oxfordhb/9780190067397.013.42 ◽

2020 ◽

pp. 667-683

Author(s):

Bryant Walker Smith

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Ethical Question ◽

Power Dynamics ◽

Automated Vehicles ◽

Automated Driving ◽

The U.S ◽

Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

Download Full-text

Eye Movements in Response to Pain-Related Feelings in the Presence of Low and High Cognitive Loads

Behavioral Sciences ◽

10.3390/bs10050092 ◽

2020 ◽

Vol 10 (5) ◽

pp. 92

Author(s):

Ramtin Zargari Marandi ◽

Camilla Ann Fjelsted ◽

Iris Hrustanovic ◽

Rikke Dan Olesen ◽

Parisa Gazerani

Keyword(s):

Eye Movements ◽

Cognitive Load ◽

Eye Movement ◽

Cognitive Processing ◽

Pain Perception ◽

Cognitive Task ◽

Load Level ◽

Task Load ◽

Pupillary Responses ◽

Movement Characteristics

The affective dimension of pain contributes to pain perception. Cognitive load may influence pain-related feelings. Eye tracking has proven useful for detecting cognitive load effects objectively by using relevant eye movement characteristics. In this study, we investigated whether eye movement characteristics differ in response to pain-related feelings in the presence of low and high cognitive loads. A set of validated, control, and pain-related sounds were applied to provoke pain-related feelings. Twelve healthy young participants (six females) performed a cognitive task at two load levels, once with the control and once with pain-related sounds in a randomized order. During the tasks, eye movements and task performance were recorded. Afterwards, the participants were asked to fill out questionnaires on their pain perception in response to the applied cognitive loads. Our findings indicate that an increased cognitive load was associated with a decreased saccade peak velocity, saccade frequency, and fixation frequency, as well as an increased fixation duration and pupil dilation range. Among the oculometrics, pain-related feelings were reflected only in the pupillary responses to a low cognitive load. The performance and perceived cognitive load decreased and increased, respectively, with the task load level and were not influenced by the pain-related sounds. Pain-related feelings were lower when performing the task compared with when no task was being performed in an independent group of participants. This might be due to the cognitive engagement during the task. This study demonstrated that cognitive processing could moderate the feelings associated with pain perception.

Download Full-text

Automated vehicles and the morality of post-collision behavior

Ethics and Information Technology ◽

10.1007/s10676-021-09607-w ◽

2021 ◽

Author(s):

Sebastian Krügel ◽

Matthias Uhl ◽

Bryn Balcombe

Keyword(s):

European Commission ◽

Research Program ◽

Collision Detection ◽

Expert Group ◽

Automated Vehicles ◽

Automated Driving ◽

Data Provision

AbstractWe address the considerations of the European Commission Expert Group on the ethics of connected and automated vehicles regarding data provision in the event of collisions. While human drivers’ appropriate post-collision behavior is clearly defined, regulations for automated driving do not provide for collision detection. We agree it is important to systematically incorporate citizens’ intuitions into the discourse on the ethics of automated vehicles. Therefore, we investigate whether people expect automated vehicles to behave like humans after an accident, even if this behavior does not directly affect the consequences of the accident. We find that appropriate post-collision behavior substantially influences people’s evaluation of the underlying crash scenario. Moreover, people clearly think that automated vehicles can and should record the accident, stop at the site, and call the police. They are even willing to pay for technological features that enable post-collision behavior. Our study might begin a research program on post-collision behavior, enriching the empirically informed study of automated driving ethics that so far exclusively focuses on pre-collision behavior.

Download Full-text

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

it - Information Technology ◽

10.1515/itit-2020-0023 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jonas Andersson ◽

Azra Habibovic ◽

Daban Rizgary

Keyword(s):

Driver Behavior ◽

Added Value ◽

Future Research ◽

Gaze Behavior ◽

Repeated Testing ◽

Automated Vehicles ◽

Automated Driving ◽

Perceived Safety ◽

Repeated Interaction ◽

Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

Download Full-text

The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Electronics ◽

10.3390/electronics10141737 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1737

Author(s):

Wooseop Lee ◽

Min-Hee Kang ◽

Jaein Song ◽

Keeyeon Hwang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Processing Speed ◽

Model Comparison ◽

Distance Estimation ◽

Visual Object ◽

Suitable Model ◽

Automated Vehicles ◽

Automated Driving

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

Download Full-text

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994580 ◽

2021 ◽

pp. 036119812199458

Author(s):

Slobodan Gutesa ◽

Joyoung Lee ◽

Dejan Besenski

Keyword(s):

Travel Time ◽

Information Exchange ◽

Fixed Time ◽

Optimization Method ◽

Time Signal ◽

Market Penetration ◽

Automated Vehicles ◽

Automated Driving ◽

Concept Evaluation ◽

Intersection Management

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

Download Full-text

Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

Open Engineering ◽

10.1515/eng-2021-0079 ◽

2021 ◽

Vol 11 (1) ◽

pp. 845-852

Author(s):

Aleksandra Rodak ◽

Paweł Budziszewski ◽

Małgorzata Pędzierska ◽

Mikołaj Kruszewski

Keyword(s):

Performance Assessment ◽

Minimal Risk ◽

Automated Vehicles ◽

Automated Driving ◽

Driving System ◽

Driving Task ◽

Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

Download Full-text

How cognitive loads modulate the postural control of older women with low back pain?

BMC Geriatrics ◽

10.1186/s12877-021-02025-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Le Ge ◽

Qiuhua Yu ◽

Chuhuai Wang ◽

Huanjie Huang ◽

Xin Li ◽

...

Keyword(s):

Low Back Pain ◽

Postural Control ◽

Older Women ◽

Cognitive Task ◽

Difficulty Level ◽

Control Group ◽

Low Back ◽

Dual Tasks ◽

Balance Test ◽

Arithmetic Task

Abstract Background The capacity of postural control is a key factor related to falling in older people, particularly in older women with low back pain (LBP). Cognitive involvement in postural control increases with age. However, most scholars have not considered different difficulty levels of cognitive loads when exploring the effects of cognition on postural control in older patients with LBP. The present study is to investigate how different levels of cognitive loads modulate postural control in older women with LBP. Methods This was a cross-sectional study. Twenty older women with LBP were recruited into the LBP group, and 20 healthy older women without the history of LBP were recruited into the healthy control group. Balance parameters were computed to quantify postural control. All participants underwent the balance test, which required the participant to maintain stability during standing on a force platform with or without a concurrent cognitive task. The balance test included three levels of difficulties of posture tasks (eyes-open vs. eyes-closed vs. one-leg stance) and three cognitive tasks (without cognitive task vs. auditory arithmetic task vs. serial-7 s arithmetic task). Results A repeated-measure analysis of variance (3 postural tasks × 3 congnitive tasks× 2 groups) testing the effects of the different congnitive task levels on the performance in different postural conditions. Older women with LBP had worse postural control (as reflected by larger center of pressure (COP) parameters) than control group regardless of postural or cognitive difficulties. Compared with the single task, the COP parameters of participants with LBP were larger during dual tasks, even though the difficulty level of the cognitive task was low. Larger COP parameters were shown only if the difficulty level of the cognitive task was high in control group. Correlations between sway area/sway length and the number of falls were significant in dual tasks. Conclusion Our findings shed light on how cognitive loads modulate postural control for older women with LBP. Compared with control group, cognitive loads showed more disturbing effects on postural control in older women with LBP, which was associated with falling.

Download Full-text