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 …
Visual point cloud forecasting enables scalable autonomous driving
In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …
autonomous driving remains seldom explored. Visual autonomous driving applications …
Leveraging vision-centric multi-modal expertise for 3d object detection
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object
detectors (apprentice) through the knowledge transferred from LiDAR-or multi-modal-based …
detectors (apprentice) through the knowledge transferred from LiDAR-or multi-modal-based …
Openlane-v2: A topology reasoning benchmark for unified 3d hd mapping
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles
to execute correct judgments. However, existing benchmarks tend to oversimplify the scene …
to execute correct judgments. However, existing benchmarks tend to oversimplify the scene …
Sparseocc: Rethinking sparse latent representation for vision-based semantic occupancy prediction
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space
where 2D latent representations are mapped and subsequent 3D operators are applied …
where 2D latent representations are mapped and subsequent 3D operators are applied …
Human-centric autonomous systems with llms for user command reasoning
The evolution of autonomous driving has made remarkable advancements in recent years,
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …
evolving into a tangible reality. However, a human-centric large-scale adoption hinges on …
MoST: Multi-modality Scene Tokenization for Motion Prediction
Many existing motion prediction approaches rely on symbolic perception outputs to generate
agent trajectories such as bounding boxes road graph information and traffic lights. This …
agent trajectories such as bounding boxes road graph information and traffic lights. This …
Towards knowledge-driven autonomous driving
This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …
investigation highlights the limitations of current autonomous driving systems, in particular …
End-to-end autonomous driving using deep learning: A systematic review
A Singh - arXiv preprint arXiv:2311.18636, 2023 - arxiv.org
End-to-end autonomous driving is a fully differentiable machine learning system that takes
raw sensor input data and other metadata as prior information and directly outputs the ego …
raw sensor input data and other metadata as prior information and directly outputs the ego …
Think2drive: Efficient reinforcement learning by thinking in latent world model for quasi-realistic autonomous driving (in carla-v2)
Real-world autonomous driving (AD) especially urban driving involves many corner cases.
The lately released AD simulator CARLA v2 adds 39 common events in the driving scene …
The lately released AD simulator CARLA v2 adds 39 common events in the driving scene …