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 3576–3600 of 6092 papers

TitleStatusHype
Three Cars Approaching within 100m! Enhancing Distant Geometry by Tri-Axis Voxel Scanning for Camera-based Semantic Scene Completion—0
TiCoSS: Tightening the Coupling between Semantic Segmentation and Stereo Matching within A Joint Learning Framework—0
TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems—0
TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric—0
Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving—0
Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving—0
Time-Dependent Performance Modeling for Platooning Communications at Intersection—0
TimeLDM: Latent Diffusion Model for Unconditional Time Series Generation—0
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems—0
Time of Arrival Error Estimation for Positioning Using Convolutional Neural Networks—0
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection—0
Tiny Deep Ensemble: Uncertainty Estimation in Edge AI Accelerators via Ensembling Normalization Layers with Shared Weights—0
TinyDrive: Multiscale Visual Question Answering with Selective Token Routing for Autonomous Driving—0
TIP: Task-Informed Motion Prediction for Intelligent Vehicles—0
TLD: A Vehicle Tail Light signal Dataset and Benchmark—0
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving—0
TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving—0
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving—0
To Make Yourself Invisible with Adversarial Semantic Contours—0
Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning—0
TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior—0
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters—0
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module—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