Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network
B Dong, H Liu, Y Bai, J Lin, Z Xu, X Xu… - arXiv preprint arXiv …, 2021 - arxiv.org
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous
driving cars to achieve a high degree of road safety. There are several challenges in …
driving cars to achieve a high degree of road safety. There are several challenges in …
Motion forecasting with unlikelihood training in continuous space
Motion forecasting is essential for making safe and intelligent decisions in robotic
applications such as autonomous driving. Existing methods often formulate it as a sequence …
applications such as autonomous driving. Existing methods often formulate it as a sequence …
Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset
As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …
attention as a critical requirement for planning. Of particular importance are interactive …
Motionnet: Joint perception and motion prediction for autonomous driving based on bird's eye view maps
The ability to reliably perceive the environmental states, particularly the existence of objects
and their motion behavior, is crucial for autonomous driving. In this work, we propose an …
and their motion behavior, is crucial for autonomous driving. In this work, we propose an …
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 …
Motiondiffuser: Controllable multi-agent motion prediction using diffusion
We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …
trajectories over multiple agents. Such representation has several key advantages: first, our …
Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
Maneuver-aware pooling for vehicle trajectory prediction
Autonomous vehicles should be able to predict the future states of its environment and
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …
respond appropriately. Specifically, predicting the behavior of surrounding human drivers is …
Motion prediction using trajectory cues
Predicting human motion from a historical pose sequence is at the core of many applications
in computer vision. Current state-of-the-art methods concentrate on learning motion contexts …
in computer vision. Current state-of-the-art methods concentrate on learning motion contexts …
Multimodal trajectory predictions for autonomous driving using deep convolutional networks
Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …
intelligence communities are facing at the moment, both in terms of difficulty and potential …