Query-centric trajectory prediction
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 …
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
Argoverse 2: Next generation datasets for self-driving perception and forecasting
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 …
research in the self-driving domain. The annotated Sensor Dataset contains 1,000 …
Rethinking integration of prediction and planning in deep learning-based automated driving systems: a review
Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Besides the enormous challenge of perception, ie accurately perceiving the environment …
Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders
This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …
forecasting, an area that has not yet been extensively investigated despite the widespread …
Parting with misconceptions about learning-based vehicle motion planning
The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …
large-scale real-world dataset and evaluation schemes requiring both precise short-term …
Prophnet: Efficient agent-centric motion forecasting with anchor-informed proposals
Motion forecasting is a key module in an autonomous driving system. Due to the
heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low …
heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low …
Real-time motion prediction via heterogeneous polyline transformer with relative pose encoding
The real-world deployment of an autonomous driving system requires its components to run
on-board and in real-time, including the motion prediction module that predicts the future …
on-board and in real-time, including the motion prediction module that predicts the future …
Implicit occupancy flow fields for perception and prediction in self-driving
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future
behavior of other traffic participants. Existing works either perform object detection followed …
behavior of other traffic participants. Existing works either perform object detection followed …
Mixsim: A hierarchical framework for mixed reality traffic simulation
The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …
Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing
Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …
evaluate autonomous vehicle performance before deployment. This requires simulating …