Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Morales-Alvarez, Walter; Sipele, Óscar; Léberon, Régis; Tadjine, Hadj Hamma; Olaverri-Monreal, Cristina

doi:10.3390/electronics9122087

Cited by 79 publications

(31 citation statements)

References 88 publications

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“…Performing surgery is not unlike driving a vehicle, in that decisions and maneuvers must be made based on visual information. In recent years, the technologies behind autonomous driving systems that utilize artificial intelligence (AI) technology, especially deep-learning algorithms, have progressed rapidly 7 . One benefit that AI offers in that field is fewer traffic accidents due to human error, by predicting safe driving lanes based on obstacle recognition (e.g., of other vehicles, traffic lights, road signs, and pedestrians).…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy

Kumazu

Kobayashi²,

Kitamura³

et al. 2021

Sci Rep

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The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons’ experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335–0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45–3.95). The mean misrecognition score was a low 0.14 (range 0–0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy

Kumazu

Kobayashi²,

Kitamura³

et al. 2021

Sci Rep

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show abstract

“…For example, during conditional automation, a timely response from a driver during a take-over request in the event of an unexpected situation is crucial to ensure road safety. To this end, the effect of nondriving tasks in a real-world environment is assessed, taking into account a driver's state and the environment [4], [5].…”

Section: Human Factors and Interaction With Intelligent Vehiclesmentioning

confidence: 99%

Chair for Intelligent Transportation Systems–Sustainable Transport Logistics 4.0

Lv¹

2021

IEEE Intell. Transport. Syst. Mag.

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“…In these situations, a TOR can be a source of confusion when signaled. When a TOR is issued, the driver performing NDRTs should recognize the situation immediately and prepare themselves for manual driving [8].…”

Section: Introductionmentioning

confidence: 99%

Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

Han

2021

Electronics

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In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms.

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Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Cited by 79 publications

References 88 publications

Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy

Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy

Chair for Intelligent Transportation Systems–Sustainable Transport Logistics 4.0

Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

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