Vehicle trajectory prediction based on intention-aware non-autoregressive transformer with multi-attention learning for Internet of Vehicles
X Chen, H Zhang, F Zhao, Y Cai… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …
Bat: Behavior-aware human-like trajectory prediction for autonomous driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
A review on intention-aware and interaction-aware trajectory prediction for autonomous vehicles
This paper presents a literature review on Intention-aware and Interaction-aware Trajectory
Prediction for Autonomous Vehicle, which covers primary studies since 2008. The research …
Prediction for Autonomous Vehicle, which covers primary studies since 2008. The research …
VNAGT: Variational non-autoregressive graph transformer network for multi-agent trajectory prediction
X Chen, H Zhang, Y Hu, J Liang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging
because the movement patterns of agents are complex and stochastic, not only depending …
because the movement patterns of agents are complex and stochastic, not only depending …
A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle
Vehicle trajectory prediction is essential in ensuring the safe and efficient operation of
advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables …
advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables …
Improving Efficiency and Generalisability of Motion Predictions With Deep Multi-Agent Learning and Multi-Head Attention
Automated Vehicles (AVs) have been receiving increasing attention as a potential highly
mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to …
mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to …
Vehicle Trajectory Prediction Using Deep Learning for Advanced Driver Assistance Systems and Autonomous Vehicles
Vehicle trajectory prediction is essential for advanced driver assistance systems (ADAS) and
autonomous vehicles (AVs), playing a crucial role in collision avoidance, path planning, and …
autonomous vehicles (AVs), playing a crucial role in collision avoidance, path planning, and …
面向群体行驶场景的时空信息融合车辆轨迹预测…
李立， 平振东， 朱进玉， 徐志刚， 汪贵平 - 交通运输工程学报, 2022 - transport.chd.edu.cn
将车辆间时空交互信息融入卷积社会池化网络中, 提出了一种面向群体行驶场景的有人驾驶车辆
轨迹预测模型; 使用长短时记忆(LSTM) 网络预测群体车辆速度, 基于此预测值计算群体车辆间的 …
轨迹预测模型; 使用长短时记忆(LSTM) 网络预测群体车辆速度, 基于此预测值计算群体车辆间的 …
Stochastic Non-Autoregressive Transformer-Based Multi-Modal Pedestrian Trajectory Prediction for Intelligent Vehicles
X Chen, H Zhang, F Deng, J Liang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Pedestrian trajectory prediction, which aims at predicting the future positions of all
pedestrians in a crowd scene given their past trajectories, is the cornerstone of autonomous …
pedestrians in a crowd scene given their past trajectories, is the cornerstone of autonomous …
STI-TP: A Spatio-temporal interleaved model for multi-modal trajectory prediction of heterogeneous traffic agents
Y Xu, Q Jia, H Wang, C Ji, X Li, Y Li, F Chen - Computers and Electrical …, 2024 - Elsevier
Trajectory prediction for heterogeneous traffic agents in autonomous driving is a challenging
and crucial task. A large amount of research has laid a solid foundation for this field …
and crucial task. A large amount of research has laid a solid foundation for this field …