In-Cabin Monitoring System for Autonomous Vehicles

Mishra, Ashutosh; Lee, Sang-Ho; Kim, Dohyun; Kim, Shiho

doi:10.3390/s22124360

Cited by 12 publications

(1 citation statement)

References 30 publications

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“…Driver distraction is detected by examining the driver's attention to the driving process and alerting them in scenarios of attention loss [1][2][3][4][5]. Drowsiness is detected by monitoring the facial expression of the driver based on computer-visionbased techniques [6][7][8][9][10][11] or by measuring the physiological signals of the driver using an electrocardiogram (ECG) [12] and heart rate monitoring [13]. Most driver-behaviormonitoring methods have been designed to reduce crash-related accidents by addressing driver behavior.…”

Section: Introductionmentioning

confidence: 99%

Visual-Based Children and Pet Rescue from Suffocation and Incidence of Hyperthermia Death in Enclosed Vehicles

Moussa

Shoitan

Cho

et al. 2023

Sensors

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Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.

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