Table 3 Summary of research on body posture classification

[85]

Proposes an ensemble model using deep convnets for Human Body Posture Recognition (HBPR), relevant for posture-based analysis in autonomous vehicle cabins.

Bivarial Deep Convnet Model

Deep convnets in the ensemble model for HBPR, useful for posture analysis in autonomous vehicles.

[86]

Classifies seven common driving activities using pre-trained CNN models, contributing to driver behavior analysis within autonomous vehicles.

AlexNet, GoogLeNet, ResNet50

Uses AlexNet, GoogLeNet, ResNet50 to classify driving activities, aiding in driver behavior analysis in autonomous vehicles.

[87]

Proposes a drone surveillance system for human behavior analysis, including posture analysis via OpenPose, relevant for outdoor surveillance and interaction with autonomous vehicles.

Pose Estimation, OpenPose, Deep- Sort, YOLO

Applies OpenPose and other algorithms for human behavior analysis, useful in autonomous vehicle interactions.

[88]

Utilizes PoseNet for real-time 6-DOF camera relocalization from single RGB images, relevant for vehicle cabin monitoring and driver pose estimation.

23-layer deep CNN (convolutional neural network) trained in an end-to-end manner to regress the 6-DOF camera pose.

Utilizes PoseNet for 6-DOF camera relocalization, aiding in driver pose estimation in vehicle cabins.

[45]

Uses MoveNet to predict subject-specific joint angle profiles for different walking conditions, applicable for pedestrian behavior analysis around autonomous vehicles.

MoveNet

MoveNet predicts joint angles for pedestrian analysis, useful for passenger behavior study in autonomous vehicles.

[89]

Presents D3-Guard, a real-time drowsy driving detection system using built-in smartphone audio devices and LSTM networks, applicable for drowsiness monitoring in autonomous vehicle cabins.

LSTM networks

D3-Guard detects driver drowsiness using smartphone audio and LSTM, relevant for monitoring in autonomous vehicles.

[90]

Introduces BiRSwinT network for fine-grained driver behavior recognition, offering enhanced driver action learning and accuracy in driver behavior analysis.

Bilinear full-scale residual Swin-Transformer network (BiRSwinT)

BiRSwinT network enhances fine-grained driver behavior recognition, contributing to in-cabin analysis.

[91]

Proposes a driver distraction detection system using a blend of deep learning and machine learning models, relevant for enhancing roadway safety through distraction monitoring.

DenseNet and Genetic Algorithms (GA)

TCombines DenseNet and GA for driver distraction detection, focusing on posture-based insights for safety in autonomous vehicles.

[76]

Leverages MobileNetV2 for efficient classification of driver distraction behaviors, demonstrating potential for reducing accidents caused by distracted driving in autonomous vehicles.

MobileNetV2

MobileNetV2 classifies driver distractions by detecting subtle posture changes, aiding vision systems in autonomous vehicles.