Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Improving multi-agent trajectory prediction using traffic states on interactive driving scenarios
Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …
intersections, and roundabouts are challenging due to the high density of agents, varying …
A Review of Decision-Making and Planning for Autonomous Vehicles in Intersection Environments
S Chen, X Hu, J Zhao, R Wang, M Qiao - World Electric Vehicle Journal, 2024 - mdpi.com
Decision-making and planning are the core aspects of autonomous driving systems. These
factors are crucial for improving the safety, driving experience, and travel efficiency of …
factors are crucial for improving the safety, driving experience, and travel efficiency of …
Map** the evolution of cybernetics: a bibliometric perspective
In this study, we undertake a comprehensive bibliometric analysis of the cybernetics
research field. We compile a dataset of 4856 papers from the ISI Web of Science database …
research field. We compile a dataset of 4856 papers from the ISI Web of Science database …
Hi-SCL: Fighting long-tailed challenges in trajectory prediction with hierarchical wave-semantic contrastive learning
Predicting the future trajectories of traffic agents is a pivotal aspect in achieving collision-free
driving for autonomous vehicles. Although the overall accuracy of existing prediction …
driving for autonomous vehicles. Although the overall accuracy of existing prediction …
Learning vehicle trajectory uncertainty
A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The
filter utilizes recurrent neural networks to learn the vehicle's geometrical and kinematic …
filter utilizes recurrent neural networks to learn the vehicle's geometrical and kinematic …
A temporal multi-gate mixture-of-experts approach for vehicle trajectory and driving intention prediction
R Yuan, M Abdel-Aty, Q **ang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurate vehicle trajectory prediction is critical for autonomous vehicles and advanced
driver assistance systems to make driving decisions and improve traffic safety. This paper …
driver assistance systems to make driving decisions and improve traffic safety. This paper …
Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction
Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic
flow control. However the heterogeneous traffic density on interactions which caused by …
flow control. However the heterogeneous traffic density on interactions which caused by …
Optimized Long Short-Term Memory Network for LiDAR-Based Vehicle Trajectory Prediction Through Bayesian Optimization
In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on
user expertise and prior knowledge, while newer deep learning approaches, such as Long …
user expertise and prior knowledge, while newer deep learning approaches, such as Long …
[HTML][HTML] Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints
The accurate prediction of vehicle behavior is crucial for autonomous driving systems,
impacting their safety and efficiency in complex urban environments. To address the …
impacting their safety and efficiency in complex urban environments. To address the …