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

Showing 76100 of 6092 papers

TitleStatusHype
SMART: Scalable Multi-agent Real-time Motion Generation via Next-token PredictionCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous DrivingCode3
Vision-based 3D occupancy prediction in autonomous driving: a review and outlookCode3
NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous DrivingCode3
HPNet: Dynamic Trajectory Forecasting with Historical Prediction AttentionCode3
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR SynthesisCode3
Producing and Leveraging Online Map Uncertainty in Trajectory PredictionCode3
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object DetectionCode3
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object DetectionCode3
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video GenerationCode3
Embodied Understanding of Driving ScenariosCode3
Behavior Generation with Latent ActionsCode3
Leveraging Enhanced Queries of Point Sets for Vectorized Map ConstructionCode3
GenAD: Generative End-to-End Autonomous DrivingCode3
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-AgentsCode3
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous DrivingCode3
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous DrivingCode3
DeFlow: Decoder of Scene Flow Network in Autonomous DrivingCode3
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and OpportunitiesCode3
DriveLM: Driving with Graph Visual Question AnsweringCode3
Mind the map! Accounting for existing map information when estimating online HDMaps from sensorCode3
LLM4Drive: A Survey of Large Language Models for Autonomous DrivingCode3
TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUsCode3
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Benchmark Results

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