Generic tracking and probabilistic prediction framework and its application in autonomous driving
Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for
intelligent systems such as autonomous vehicles to achieve safe and high-quality decision …
intelligent systems such as autonomous vehicles to achieve safe and high-quality decision …
A taxonomy and review of algorithms for modeling and predicting human driver behavior
K Brown, K Driggs-Campbell… - arXiv preprint arXiv …, 2020 - arxiv.org
We present a review and taxonomy of 200 models from the literature on driver behavior
modeling. We begin by introducing a mathematical framework for describing the dynamics of …
modeling. We begin by introducing a mathematical framework for describing the dynamics of …
Multi-agent driving behavior prediction across different scenarios with self-supervised domain knowledge
How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …
autonomous driving. It is significant to have the ability to predict surrounding road …
Grouptron: Dynamic multi-scale graph convolutional networks for group-aware dense crowd trajectory forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive
scenes is a longstanding challenge. Recent advances in using data-driven approaches …
scenes is a longstanding challenge. Recent advances in using data-driven approaches …
Rmp: A random mask pretrain framework for motion prediction
As the pretraining technique is growing in popularity, little work has been done on pretrained
learning-based motion prediction methods in autonomous driving. In this paper, we propose …
learning-based motion prediction methods in autonomous driving. In this paper, we propose …
Kinematics-aware multigraph attention network with residual learning for heterogeneous trajectory prediction
Trajectory prediction for heterogeneous traffic agents plays a crucial role in ensuring the
safety and efficiency of automated driving in highly interactive traffic environments …
safety and efficiency of automated driving in highly interactive traffic environments …
A hierarchical motion planning framework for autonomous driving in structured highway environments
This paper presents an efficient hierarchical motion planning framework with a long
planning horizon for autonomous driving in structured environments. A 3D motion planning …
planning horizon for autonomous driving in structured environments. A 3D motion planning …
Interactive prediction for multiple, heterogeneous traffic participants with multi-agent hybrid dynamic bayesian network
Interactive prediction with multiple traffic participants in highly dynamic scenarios is
extremely challenging for autonomous driving, especially when heterogeneous agents such …
extremely challenging for autonomous driving, especially when heterogeneous agents such …
Interaction-aware behavior planning for autonomous vehicles validated with real traffic data
Autonomous vehicles (AVs) need to interact with other traffic participants who can be either
cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to …
cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to …
Multi-vehicle trajectory prediction and control at intersections using state and intention information
Traditional deep learning approaches for prediction of future trajectory of multiple road
agents rely on knowing information about their past trajectory. In contrast, this work utilizes …
agents rely on knowing information about their past trajectory. In contrast, this work utilizes …