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

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Showing 1–50 of 6092 papers

TitleStatusHype
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and BenchmarkingCode7
GenAD: Generalized Predictive Model for Autonomous DrivingCode7
Vista: A Generalizable Driving World Model with High Fidelity and Versatile ControllabilityCode7
AWQ: Activation-aware Weight Quantization for LLM Compression and AccelerationCode6
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
The Role of World Models in Shaping Autonomous Driving: A Comprehensive SurveyCode5
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian SplattingCode5
Awesome Multi-modal Object TrackingCode5
Neural Fields in Robotics: A SurveyCode5
Getting SMARTER for Motion Planning in Autonomous Driving SystemsCode5
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth EstimationCode5
DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous DrivingCode5
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric PerspectivesCode5
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic PlanningCode5
Pseudo-Simulation for Autonomous DrivingCode4
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense PredictionCode4
Senna: Bridging Large Vision-Language Models and End-to-End Autonomous DrivingCode4
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy PredictionCode4
Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous DrivingCode4
UltimateDO: An Efficient Framework to Marry Occupancy Prediction with 3D Object Detection via Channel2heightCode4
A Survey on Vision-Language-Action Models for Autonomous DrivingCode4
SparseDrive: End-to-End Autonomous Driving via Sparse Scene RepresentationCode4
End-to-end Autonomous Driving: Challenges and FrontiersCode4
UniScene: Unified Occupancy-centric Driving Scene GenerationCode4
Segment and Track AnythingCode4
End-to-End Autonomous Driving through V2X CooperationCode4
Diffusion-Based Planning for Autonomous Driving with Flexible GuidanceCode4
GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy PredictionCode4
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal TransformersCode4
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View RepresentationCode4
S^3Gaussian: Self-Supervised Street Gaussians for Autonomous DrivingCode4
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action ModelCode4
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual ReasoningCode4
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous DrivingCode4
OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous DrivingCode4
Multimodal Chain-of-Thought Reasoning: A Comprehensive SurveyCode4
3D Scene Generation: A SurveyCode4
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and RecipeCode4
Is Sora a World Simulator? A Comprehensive Survey on General World Models and BeyondCode4
Planning-oriented Autonomous DrivingCode4
A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion PerspectiveCode4
Geometric-aware Pretraining for Vision-centric 3D Object DetectionCode3
Generalizing Motion Planners with Mixture of Experts for Autonomous DrivingCode3
Generalized Trajectory Scoring for End-to-end Multimodal PlanningCode3
Generative AI for Autonomous Driving: Frontiers and OpportunitiesCode3
GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial UnderstandingCode3
GenAD: Generative End-to-End Autonomous DrivingCode3
Enhancing End-to-End Autonomous Driving with Latent World ModelCode3
Epona: Autoregressive Diffusion World Model for Autonomous DrivingCode3
Behavior Generation with Latent ActionsCode3
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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