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 11761200 of 6092 papers

TitleStatusHype
Deep Learning for Vision-based Prediction: A SurveyCode1
DVI: Depth Guided Video Inpainting for Autonomous DrivingCode1
Continual Learning for Image-Based Camera LocalizationCode1
Deep Learning for 3D Point Cloud Understanding: A SurveyCode1
Dynamic Conditional Imitation Learning for Autonomous DrivingCode1
Deep Learning for 3D Point Clouds: A SurveyCode1
OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D DataCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
Multi-View Adaptive Fusion Network for 3D Object DetectionCode1
SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation ModelsCode1
Continuity Preserving Online CenterLine Graph LearningCode1
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous DrivingCode1
Multi-View Radar Semantic SegmentationCode1
MUVO: A Multimodal World Model with Spatial Representations for Autonomous DrivingCode1
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point CloudsCode1
NeFSAC: Neurally Filtered Minimal SamplesCode1
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous DrivingCode1
Efficient Baselines for Motion Prediction in Autonomous DrivingCode1
Deep Federated Learning for Autonomous DrivingCode1
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference SpeedCode1
Multi-source Domain Adaptation for Semantic SegmentationCode1
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR MapsCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
Multiscale Domain Adaptive YOLO for Cross-Domain Object DetectionCode1
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous DrivingCode1
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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