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 …
Z Zhou, J Wang, YH Li… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions. Existing works explore to directly predict future trajectories based on …
Z Zhou, L Ye, J Wang, K Wu… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Accurately predicting the future motions of surrounding traffic agents is critical for the safety of autonomous vehicles. Recently, vectorized approaches have dominated the motion …
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 …
J Gu, C Sun, H Zhao - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Due to the stochasticity of human behaviors, predicting the future trajectories of road agents is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …
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 …
Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene …
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 …