Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
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 …
Lmdrive: Closed-loop end-to-end driving with large language models
Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …
struggle and can incur serious accidents when encountering long-tail unforeseen events …
Motionlm: Multi-agent motion forecasting as language modeling
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 …
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …
Think twice before driving: Towards scalable decoders for end-to-end autonomous driving
End-to-end autonomous driving has made impressive progress in recent years. Existing
methods usually adopt the decoupled encoder-decoder paradigm, where the encoder …
methods usually adopt the decoupled encoder-decoder paradigm, where the encoder …
Unitr: A unified and efficient multi-modal transformer for bird's-eye-view representation
Jointly processing information from multiple sensors is crucial to achieving accurate and
robust perception for reliable autonomous driving systems. However, current 3D perception …
robust perception for reliable autonomous driving systems. However, current 3D perception …
Mtr++: Multi-agent motion prediction with symmetric scene modeling and guided intention querying
Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …
scenarios and make informed decisions. However, this task is challenging due to the diverse …
Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving
Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …
predictions of interactive behaviors between traffic participants. This paper tackles the …
Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi-object tracking
This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It
emphasizes spatio-temporal continuity and integrates both past and future reasoning for …
emphasizes spatio-temporal continuity and integrates both past and future reasoning for …
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 …