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 251275 of 6092 papers

TitleStatusHype
GAIA-1: A Generative World Model for Autonomous DrivingCode2
NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving ScenarioCode2
HGSFusion: Radar-Camera Fusion with Hybrid Generation and Synchronization for 3D Object DetectionCode2
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous DrivingCode2
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
Autonomous Driving with Spiking Neural NetworksCode2
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning MethodCode2
Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View PerceptionCode2
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane PriorsCode2
End-to-End Vectorized HD-map Construction with Piecewise Bezier CurveCode2
Enhancing Vectorized Map Perception with Historical Rasterized MapsCode2
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View TransformationCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
Asynchronous Large Language Model Enhanced Planner for Autonomous DrivingCode2
A Survey on Multimodal Large Language Models for Autonomous DrivingCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature AttentionCode2
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion DetectionCode2
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object SegmentationCode2
AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous DrivingCode2
Efficient and Robust 2D-to-BEV Representation Learning via Geometry-guided Kernel TransformerCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
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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