Format:
1 Online-Ressource (164 Seiten)
ISBN:
9783031015090
Series Statement:
Synthesis Lectures on Advances in Automotive Technology Series
Note:
Description based on publisher supplied metadata and other sources
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Cover -- Copyright page -- Title page -- Contents -- Acknowledgments -- Introduction -- Trajectory Prediction of the Vehicle -- Motion-Based Trajectory Prediction Methods -- Maneuver-Based Trajectory Prediction Models -- Interaction-Aware Trajectory Prediction Models -- Intention and Trajectory Prediction of the Pedestrians -- Driving Behavior Recognition -- Driving Styles -- Driver Characteristics Related to Risky Driving Behaviors -- Demographics -- Sensation Seeking -- Risk Perception -- Motivations -- Trajectory Prediction of the Surrounding Vehicle -- Methodologies of the Trajectory Prediction -- The Trajectory Prediction Model Based on Kinematics -- The Trajectory Prediction Model Based on the Gaussian Process -- The Trajectory Prediction Model Based on the 3IAP -- Experiments and Results -- Ablation Experiments -- Case Study -- Summary -- Predictions of the Intention and Future Trajectory of the Pedestrian -- Data Preparation -- Intention Prediction of Pedestrians -- Extraction and Processing of Skeleton Features -- Extraction and Processing of Head Orientation -- Feature Fusion Method -- LSTM Pedestrian Intention Prediction Network Based on Multiple Features -- Experiment and Analysis -- Trajectory Prediction of Pedestrians -- Characteristics and Preprocessing Methods of Pedestrian Trajectory Data -- Pedestrian Trajectory Prediction Based on the Kinematics Model -- LSTM Pedestrian Trajectory Prediction Network Based on the Enhanced Attention Mechanism -- Evaluation of the Hierarchical Pedestrian Trajectory Prediction Framework Incorporating Pedestrian Intentions -- Experiment and Analysis -- Case Study -- Summary -- Driver Secondary Driving Task Behavior Recognition -- Driver Behavior Dataset Design -- Data Collection Procedure -- Data Preprocessing -- Driver Activity Recognition Using Spatial-Temporal Graph Convolutional LSTM Network
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Spatial-Temporal Graph Convolutional LSTM Networks -- Temporal LSTM -- Model Evaluation -- Comparative Study and Real-Time Application -- Summary -- Car-Following Driving Style Classification -- Data Preparation -- Car-Following Event Data Extraction -- Data Smoothing -- Performance Indicators for Car-Following Driving Styles Identification -- Statistical Features in the Time Domain -- Statistical Features in the Frequency Domain -- Principal Component Analysis -- Classification of Driving Styles Based on the Gaussian Mixture Model -- Gaussian Mixture Model -- Clustering Results and Evaluations -- Influence of the Driving Environmental Factors on Car-Following Driving Style -- Extraction of Car-Following Events -- Car-Following Driving Style Clustering Regardless of the Driving Environment -- Car-Following Driving Style Clustering Considering the Driving Environment -- Summary -- Driving Behavior Analysis Based on Naturalistic Driving Data -- Subjective Self-Reported Risky Driving Behaviors Analysis -- Data Acquisition -- Measurements of Risky Driving Behaviors Based on the DBQ -- Factor Analysis -- Methodologies -- The Relationship Among Drivers' Driving Experience, Psychological Factors, and Risky Driving Behaviors -- The Moderating Relationship Between Drivers' Characteristics and Risky Driving Behaviors -- Classification of Driver's Driving Risk by Random Forrest Algorithm -- Methodologies -- Clustering of Driver's Risk Degree -- Classification of Driver's Risk Degree -- Analysis of Classification Model -- Summary -- Bibliography -- Authors' Biographies
Additional Edition:
Erscheint auch als Druck-Ausgabe Song, Xiaolin Behavior Analysis and Modeling of Traffic Participants Cham : Springer International Publishing AG,c2022 ISBN 9783031003813
Language:
English
