SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself 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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 2605 papers

TitleStatusHype
A Survey on Vision-Language-Action Models for Autonomous DrivingCode4
Pseudo-Simulation for Autonomous DrivingCode4
Building reliable sim driving agents by scaling self-playCode4
A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion PerspectiveCode4
TUMTraf V2X Cooperative Perception DatasetCode4
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous DrivingCode4
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain EstimationCode3
CarDreamer: Open-Source Learning Platform for World Model based Autonomous DrivingCode3
RoadBEV: Road Surface Reconstruction in Bird's Eye ViewCode3
Scaling Diffusion Models to Real-World 3D LiDAR Scene CompletionCode3
Explicit Interaction for Fusion-Based Place RecognitionCode3
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous DrivingCode3
Accelerating 3D Deep Learning with PyTorch3DCode3
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario AnalysisCode2
GaussRender: Learning 3D Occupancy with Gaussian RenderingCode2
Stag-1: Towards Realistic 4D Driving Simulation with Video Generation ModelCode2
Distilling Diffusion Models to Efficient 3D LiDAR Scene CompletionCode2
GSPR: Multimodal Place Recognition Using 3D Gaussian Splatting for Autonomous DrivingCode2
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making FrameworkCode2
SeFlow: A Self-Supervised Scene Flow Method in Autonomous DrivingCode2
SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point CloudCode2
DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social ExperiencesCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam VideosCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified