Bat: Behavior-aware human-like trajectory prediction for autonomous driving

H Liao, Z Li, H Shen, W Zeng, D Liao, G Li… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
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 …

Hierarchical adaptable and transferable networks (hatn) for driving behavior prediction

L Wang, Y Hu, L Sun, W Zhan, M Tomizuka… - arXiv preprint arXiv …, 2021 - arxiv.org
When autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient transferable and …

Transferable and adaptable driving behavior prediction

L Wang, Y Hu, L Sun, W Zhan, M Tomizuka… - arXiv preprint arXiv …, 2022 - arxiv.org
While autonomous vehicles still struggle to solve challenging situations during on-road
driving, humans have long mastered the essence of driving with efficient, transferable, and …

MPC-PF: Socially and spatially aware object trajectory prediction for autonomous driving systems using potential fields

NP Bhatt, A Khajepour… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Predicting object motion behaviour is a challenging but crucial task for safe decision making
and path planning for autonomous vehicles. It is challenging in large part due to the …

Context‐aware trajectory prediction for autonomous driving in heterogeneous environments

Z Li, Z Chen, Y Li, C Xu - Computer‐Aided Civil and …, 2024 - Wiley Online Library
The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …

Tnt: Target-driven trajectory prediction

H Zhao, J Gao, T Lan, C Sun, B Sapp… - … on Robot Learning, 2021 - proceedings.mlr.press
Predicting the future behavior of moving agents is essential for real world applications. It is
challenging as the intent of the agent and the corresponding behavior is unknown and …

A survey on deep-learning approaches for vehicle trajectory prediction in autonomous driving

J Liu, X Mao, Y Fang, D Zhu… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …

Learning interaction-aware motion prediction model for decision-making in autonomous driving

Z Huang, H Liu, J Wu, W Huang… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
Predicting the behaviors of other road users is crucial to safe and intelligent decision-making
for autonomous vehicles (AVs). However, most motion prediction models ignore the …

Learning interaction-aware probabilistic driver behavior models from urban scenarios

J Schulz, C Hubmann, N Morin… - 2019 IEEE intelligent …, 2019 - ieeexplore.ieee.org
Human drivers have complex and individual behavior characteristics which describe how
they act in a specific situation. Accurate behavior models are essential for many applications …

Real-time heterogeneous road-agents trajectory prediction using hierarchical convolutional networks and multi-task learning

L Li, X Wang, D Yang, Y Ju, Z Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Trajectory prediction of heterogeneous road agents such as vehicles, cyclists, and
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …