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 151–175 of 6092 papers

TitleStatusHype
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View TransformationCode2
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and RoadmapCode2
Exploring the Causality of End-to-End Autonomous DrivingCode2
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and SegmentationCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
FocalFormer3D : Focusing on Hard Instance for 3D Object DetectionCode2
GAIA-1: A Generative World Model for Autonomous DrivingCode2
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view CamerasCode2
End-to-End Vectorized HD-map Construction with Piecewise Bezier CurveCode2
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane PriorsCode2
BEVFusion: A Simple and Robust LiDAR-Camera Fusion FrameworkCode2
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point CloudsCode2
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous DrivingCode2
BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop DrivingCode2
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object SegmentationCode2
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel TransformerCode2
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion DetectionCode2
Drone-assisted Road Gaussian Splatting with Cross-view UncertaintyCode2
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point CloudsCode2
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
Benchmarking the Robustness of LiDAR Semantic Segmentation ModelsCode2
Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and TaxonomyCode2
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous DrivingCode2
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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