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Found 11 papers, 8 papers with code
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation.
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving
End-to-end differentiable learning for autonomous driving (AD) has recently become a prominent paradigm.
LLM4Drive: A Survey of Large Language Models for Autonomous Driving
Recently, large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers.
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
We find that even equipped with a SOTA perception model, directly letting the student model learn the required inputs of the teacher model leads to poor driving performance, which comes from the large distribution gap between predicted privileged inputs and the ground-truth.
Ranked #2 on
CARLA longest6
on CARLA
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.
Ranked #4 on
CARLA longest6
on CARLA
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving.
Planning-oriented Autonomous Driving
Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step.
Ranked #3 on
Autonomous Driving
on CARLA Leaderboard
HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
Based on these observations, we propose Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges.
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
We find three interpretable patterns of interactions, bringing insights for driver behavior representation, modeling and comprehension.
