Autonomous driving has achieved significant milestones in research and development over the last decade. There is increasing interest in the field as the deployment of self-operating …
Y Huang, J Du, Z Yang, Z Zhou… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In order to drive safely in a dynamic environment, autonomous vehicles should be able to predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
We introduce Argoverse 2 (AV2)-a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …
S Ettinger, S Cheng, B Caine, C Liu… - Proceedings of the …, 2021 - openaccess.thecvf.com
As autonomous driving systems mature, motion forecasting has received increasing attention as a critical requirement for planning. Of particular importance are interactive …
Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving. Applying deep learning to this problem requires fusing …
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 …
C Vishnu, V Abhinav, D Roy… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting trajectories of multiple agents in interactive driving scenarios such as intersections, and roundabouts are challenging due to the high density of agents, varying …
With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies …