Autonomous Driving
1415 papers with code • 4 benchmarks • 66 datasets
Autonomous driving is the task of driving a vehicle without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)
Libraries
Use these libraries to find Autonomous Driving models and implementationsDatasets
Latest papers with no code
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem.
LaneCorrect: Self-supervised Lane Detection
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments.
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics.
Collaborative Perception Datasets in Autonomous Driving: A Survey
This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Everything (V2X).
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
Localization Based on MIMO Backscattering from Retro-Directive Antenna Arrays
In this paper, we propose the exploitation of backscattering from retro-directive antenna arrays (RAAs) to address these imperatives.
Soar: Design and Deployment of A Smart Roadside Infrastructure System for Autonomous Driving
To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems.
FisheyeDetNet: Object Detection on Fisheye Surround View Camera Systems for Automated Driving
To the best of our knowledge, this is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios.
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
In this paper, we propose VisLED, a language-driven active learning framework for diverse open-set 3D Object Detection.
FipTR: A Simple yet Effective Transformer Framework for Future Instance Prediction in Autonomous Driving
The future instance prediction from a Bird's Eye View(BEV) perspective is a vital component in autonomous driving, which involves future instance segmentation and instance motion prediction.