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 2601–2625 of 6092 papers

TitleStatusHype
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change DetectionCode1
InfraParis: A multi-modal and multi-task autonomous driving datasetCode0
The Robust Semantic Segmentation UNCV2023 Challenge Results—0
Uncertainty Quantification via Neural Posterior Principal Components—0
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies—0
AutoEncoding Tree for City Generation and Applications—0
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V—0
3D Multiple Object Tracking on Autonomous Driving: A Literature Review—0
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration—0
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching ProcessCode1
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation—0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch—0
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman FilterCode1
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign RecognitionCode1
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving—0
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous DrivingCode1
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery—0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationCode1
Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses—0
Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities—0
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
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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