SOTAVerified

Autonomous Driving

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)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 6092 papers

TitleStatusHype
Robust Lane Detection with Wavelet-Enhanced Context Modeling and Adaptive Sampling—0
Predicting the Road Ahead: A Knowledge Graph based Foundation Model for Scene Understanding in Autonomous Driving—0
ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation—0
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection—0
AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents—0
M3Net: Multimodal Multi-task Learning for 3D Detection, Segmentation, and Occupancy Prediction in Autonomous DrivingCode1
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles—0
Unraveling the Effects of Synthetic Data on End-to-End Autonomous DrivingCode1
Revisiting Outage for Edge Inference Systems—0
HiLoTs: High-Low Temporal Sensitive Representation Learning for Semi-Supervised LiDAR Segmentation in Autonomous DrivingCode1
Multi-modality Anomaly Segmentation on the RoadCode0
LightLoc: Learning Outdoor LiDAR Localization at Light SpeedCode2
R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception—0
Temporal Action Detection Model Compression by Progressive Block Drop—0
Enhancing Steering Estimation with Semantic-Aware GNNs—0
Hi-ALPS -- An Experimental Robustness Quantification of Six LiDAR-based Object Detection Systems for Autonomous Driving—0
How to Promote Autonomous Driving with Evolving Technology: Business Strategy and Pricing Decision—0
MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous DrivingCode1
Panoptic-CUDAL Technical Report: Rural Australia Point Cloud Dataset in Rainy Conditions—0
Nano-3D: Metasurface-Based Neural Depth Imaging—0
AutoDrive-QA- Automated Generation of Multiple-Choice Questions for Autonomous Driving Datasets Using Large Vision-Language Models—0
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
V2X-DG: Domain Generalization for Vehicle-to-Everything Cooperative Perception—0
Generating Multimodal Driving Scenes via Next-Scene Prediction—0
USAM-Net: A U-Net-based Network for Improved Stereo Correspondence and Scene Depth Estimation using Features from a Pre-trained Image Segmentation network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95—Unverified
2InterFuserDriving Score76.18—Unverified
3TCPDriving Score75.14—Unverified
4TF++ WPDriving Score66.32—Unverified
5Learning From All Vehicles (LAV)Driving Score61.85—Unverified
6TransFuserDriving Score61.18—Unverified
7TransFuser (Reproduced)Driving Score55.04—Unverified
8TCP (Reproduced)Driving Score47.91—Unverified
9Latent TransFuserDriving Score45.2—Unverified
10GRIADDriving Score36.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17—Unverified
2TransFuserRC56.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91—Unverified
2TransFuserRC78.41—Unverified