Behavior Analysis and Modeling of Traffic Participants
- Length: 171 pages
- Edition: 1
- Language: English
- Publisher: Morgan & Claypool
- Publication Date: 2021-12-01
- ISBN-10: 1636392628
- ISBN-13: 9781636392622
- Sales Rank: #0 (See Top 100 Books)
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians’ intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles.
However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road?driver?vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition.
Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers’ demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
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 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
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