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 176–200 of 6092 papers

TitleStatusHype
GaussianPretrain: A Simple Unified 3D Gaussian Representation for Visual Pre-training in Autonomous DrivingCode2
Deep Learning-Based Point Cloud Registration: A Comprehensive Survey and TaxonomyCode2
AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous DrivingCode2
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point CloudsCode2
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View TransformationCode2
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning MethodCode2
Enhancing Vectorized Map Perception with Historical Rasterized MapsCode2
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
Enhancing 3D Lane Detection and Topology Reasoning with 2D Lane PriorsCode2
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
FRNet: Frustum-Range Networks for Scalable LiDAR SegmentationCode2
End-to-End Vectorized HD-map Construction with Piecewise Bezier CurveCode2
Fully Sparse 3D Occupancy PredictionCode2
FUTR3D: A Unified Sensor Fusion Framework for 3D DetectionCode2
Exploring the Causality of End-to-End Autonomous DrivingCode2
Asynchronous Large Language Model Enhanced Planner for Autonomous DrivingCode2
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion DetectionCode2
3D Object Detection for Autonomous Driving: A Comprehensive SurveyCode2
A Cognitive-Based Trajectory Prediction Approach for Autonomous DrivingCode2
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object SegmentationCode2
DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view RepresentationCode2
BEVFusion: A Simple and Robust LiDAR-Camera Fusion FrameworkCode2
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object DetectionCode2
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
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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