Effects of Non-driving Task Related Workload and Situational Awareness in Semi-autonomous Vehicles

Abstract In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

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Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

10.20944/preprints202102.0459.v1 ◽

2021 ◽

Author(s):

De Jong Yeong ◽

Gustavo Velasco-Hernandez ◽

John Barry ◽

Joseph Walsh

Keyword(s):

Sensor Fusion ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

Obstacle Detection ◽

Future Research ◽

Software Systems ◽

Radar Sensors ◽

Technical Performance ◽

Driving Task ◽

Single Sensor

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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Environmental Perception in Autonomous Vehicles Using Edge Level Situational Awareness

2020 10th Annual Computing and Communication Workshop and Conference (CCWC) ◽

10.1109/ccwc47524.2020.9031155 ◽

2020 ◽

Author(s):

Nima Ghafoorianfar ◽

Mehdi Roopaei

Keyword(s):

Autonomous Vehicles ◽

Situational Awareness ◽

Environmental Perception

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AWARE: A Situational Awareness Framework for Facilitating Adaptive Behavior of Autonomous Vehicles in Manufacturing

Lecture Notes in Computer Science - The Semantic Web – ISWC 2020 ◽

10.1007/978-3-030-62466-8_40 ◽

2020 ◽

pp. 651-666

Author(s):

Boulos El Asmar ◽

Syrine Chelly ◽

Nour Azzi ◽

Lynn Nassif ◽

Jana El Asmar ◽

...

Keyword(s):

Adaptive Behavior ◽

Autonomous Vehicles ◽

Situational Awareness

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The Impact of Mind Wandering on Signal Detection, Semi-autonomous Driving Performance, and Physiological Responses

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631015 ◽

2019 ◽

Vol 63 (1) ◽

pp. 2051-2055 ◽

Cited By ~ 1

Author(s):

Gaojian Huang ◽

Nade Liang ◽

Chuhao Wu ◽

Brandon J. Pitts

Keyword(s):

Signal Detection ◽

Autonomous Vehicles ◽

Response Times ◽

Physiological Responses ◽

Warning Signal ◽

Autonomous Driving ◽

Mind Wandering ◽

Driving Performance ◽

Driving Task ◽

The Impact

Significant growth in the number of autonomous vehicles is expected in the coming years. With this technology, drivers will likely begin to disengage from the driving task and often experience mind wandering. Research has examined the effects of mind wandering on manual driving performance, but little work has been done to understand its impact on autonomous driving. In addition, it is unclear what physiological measurements can reveal about mind wandering in the driving context. Therefore, the goals of this paper were to (a) understand how mind wandering affects warning signal detection, semi-autonomous driving performance, and physiological responses, and (b) develop a model to predict mind wandering. Preliminary findings suggest that mind wandering may be observed as a result of road familiarity, and that the number of driving years and response times to alerts may be suitable predictors of mind wandering. This work is expected to help inform the design of future autonomous vehicles to prevent distracted driving behaviors.

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Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

Multimodal Technologies and Interaction ◽

10.3390/mti2040068 ◽

2018 ◽

Vol 2 (4) ◽

pp. 68 ◽

Cited By ~ 7

Author(s):

Natalie T. Richardson ◽

Lukas Flohr ◽

Britta Michel

Keyword(s):

Driving Simulator ◽

Situational Awareness ◽

Automated Driving ◽

Human Machine Interaction ◽

Transport Efficiency ◽

Additional Information ◽

Level 3 ◽

Driving Task ◽

Machine Interface ◽

Vehicle Automation

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

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