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 1–25 of 6092 papers

TitleStatusHype
Vista: A Generalizable Driving World Model with High Fidelity and Versatile ControllabilityCode7
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and BenchmarkingCode7
GenAD: Generalized Predictive Model for Autonomous DrivingCode7
AWQ: Activation-aware Weight Quantization for LLM Compression and AccelerationCode6
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth EstimationCode5
Awesome Multi-modal Object TrackingCode5
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic PlanningCode5
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous DrivingCode5
Neural Fields in Robotics: A SurveyCode5
Getting SMARTER for Motion Planning in Autonomous Driving SystemsCode5
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian SplattingCode5
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric PerspectivesCode5
The Role of World Models in Shaping Autonomous Driving: A Comprehensive SurveyCode5
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and RecipeCode4
3D Scene Generation: A SurveyCode4
Is Sora a World Simulator? A Comprehensive Survey on General World Models and BeyondCode4
Multimodal Chain-of-Thought Reasoning: A Comprehensive SurveyCode4
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View RepresentationCode4
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous DrivingCode4
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal TransformersCode4
End-to-end Autonomous Driving: Challenges and FrontiersCode4
Planning-oriented Autonomous DrivingCode4
End-to-End Autonomous Driving through V2X CooperationCode4
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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