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 30513075 of 6092 papers

TitleStatusHype
Fusion is Not Enough: Single Modal Attacks on Fusion Models for 3D Object DetectionCode1
SFD2: Semantic-guided Feature Detection and DescriptionCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance FieldsCode1
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous DrivingCode2
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score FilteringCode0
Quadric Representations for LiDAR Odometry, Mapping and Localization0
A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints0
Detection of Adversarial Physical Attacks in Time-Series Image Data0
AutoCure: Automated Tabular Data Curation Technique for ML PipelinesCode0
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales0
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds0
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture0
Now You See Me: Robust approach to Partial Occlusions0
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving0
Synthetic Datasets for Autonomous Driving: A Survey0
SMART: A Decision-Making Framework with Multi-modality Fusion for Autonomous Driving Based on Reinforcement LearningCode1
Vehicle State Estimation and Prediction0
Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow0
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera DistillationCode1
Knowledge Distillation from 3D to Bird's-Eye-View for LiDAR Semantic SegmentationCode1
A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian DetectionCode0
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey0
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving0
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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