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 …
Hierarchical adaptable and transferable networks (hatn) for driving behavior prediction
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 …
driving, humans have long mastered the essence of driving with efficient transferable and …
Transferable and adaptable driving behavior prediction
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 …
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 …
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
The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …
remains a challenging task for autonomous driving due to several critical issues, such as …
Tnt: Target-driven trajectory prediction
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 …
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
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 …
issue, making urgent demands for more intelligent perception and planning systems. Self …
Learning interaction-aware motion prediction model for decision-making in autonomous driving
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 …
for autonomous vehicles (AVs). However, most motion prediction models ignore the …
Learning interaction-aware probabilistic driver behavior models from urban scenarios
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 …
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 …
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …