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 301–325 of 6092 papers

TitleStatusHype
A Survey on Multimodal Large Language Models for Autonomous DrivingCode2
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane PriorsCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Advances in 4D Generation: A SurveyCode2
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action ControlCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop DrivingCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-ViewCode2
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
BEVHeight: A Robust Framework for Vision-based Roadside 3D Object DetectionCode2
GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian SplattingCode2
BEVSpread: Spread Voxel Pooling for Bird's-Eye-View Representation in Vision-based Roadside 3D Object DetectionCode2
End-to-End Vectorized HD-map Construction with Piecewise Bezier CurveCode2
BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent SpaceCode2
Fisheye-Calib-Adapter: An Easy Tool for Fisheye Camera Model ConversionCode2
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?Code2
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view RepresentationCode2
Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and PlanningCode2
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
Human-AI Shared Control via Policy DissectionCode2
DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social ExperiencesCode2
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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