Automated driving safety - The art of conscious risk taking - minimum lateral distances to pedestrians

Böddeker, Bert; Wendorff, Wilhard von; Nguyen, Nam Hoang; Diehl, Paula; Meertens, Roland; Johannson, Rolf

doi:10.23919/date51398.2021.9473991

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The second issue is the frequency with which the drivers perform the task. Even a small task, but performed frequently, can pose a safety concern [19,20].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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“…The second issue is the frequency with which the drivers perform the task. Even a small task, but performed frequently, can pose a safety concern [19,20].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The second issue is the frequency with which the drivers perform the task. Even a task that is small, but performed frequently, can pose a safety concern [ 2 , 3 , 19 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec

Konior

Sieciński

et al. 2023

Sensors

View full text Add to dashboard Cite

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

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Contributing Factors to Consider While Defining Acceptance Criteria and Validation Targets for Assuring SOTIF in Autonomous Vehicles

Madala¹,

Krishnamoorthy²,

Gonzalez³

et al. 2022

SAE Technical Paper Series

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Automated driving safety - The art of conscious risk taking - minimum lateral distances to pedestrians

Cited by 3 publications

References 7 publications

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Contributing Factors to Consider While Defining Acceptance Criteria and Validation Targets for Assuring SOTIF in Autonomous Vehicles

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