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 3651–3700 of 6092 papers

TitleStatusHype
Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data—0
Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking—0
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks—0
Deep Depth Completion from Extremely Sparse Data: A Survey—0
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving—0
Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing—0
DeepDyve: Dynamic Verification for Deep Neural Networks—0
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation—0
Deep Evaluation Metric: Learning to Evaluate Simulated Radar Point Clouds for Virtual Testing of Autonomous Driving—0
Deep Event-based Object Detection in Autonomous Driving: A Survey—0
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection—0
Deep Grid Net (DGN): A Deep Learning System for Real-Time Driving Context Understanding—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing—0
Deep Instance Segmentation with Automotive Radar Detection Points—0
Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping—0
Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving—0
Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving—0
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer—0
Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook—0
Deep Learning Based 3D Segmentation: A Survey—0
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses—0
Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review—0
Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses—0
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey—0
Deep Learning-based Embedded Intrusion Detection System for Automotive CAN—0
Deep Learning Based Intelligent Inter-Vehicle Distance Control for 6G Enabled Cooperative Autonomous Driving—0
Deep-learning-based on-chip rapid spectral imaging with high spatial resolution—0
Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors—0
Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data—0
Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Deep learning for 3D Object Detection and Tracking in Autonomous Driving: A Brief Survey—0
Deep Learning for Embodied Vision Navigation: A Survey—0
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review—0
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review—0
Deep Learning for Multi-View Stereo via Plane Sweep: A Survey—0
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview—0
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker—0
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles—0
Deep Material Recognition in Light-Fields via Disentanglement of Spatial and Angular Information—0
DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical Driving Scenario Simulation—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Deep Multi-Sensor Lane Detection—0
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction—0
Deep Neural Network Perception Models and Robust Autonomous Driving Systems—0
Deep Neural Networks and PIDE discretizations—0
Deep Neural Networks for Computational Optical Form Measurements—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
Deep Object-Centric Policies for Autonomous Driving—0
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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