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 3876–3900 of 6092 papers

TitleStatusHype
EdgeServe: A Streaming System for Decentralized Model Serving—0
To Make Yourself Invisible with Adversarial Semantic Contours—0
AdaptiveShape: Solving Shape Variability for 3D Object Detection with Geometry Aware Anchor Distributions—0
DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving—0
Online Black-Box Confidence Estimation of Deep Neural Networks—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Revisiting Modality Imbalance In Multimodal Pedestrian Detection—0
SEO: Safety-Aware Energy Optimization Framework for Multi-Sensor Neural Controllers at the Edge—0
Multi-RIS-Enabled 3D Sidelink Positioning—0
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation—0
Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation—0
Transformer-Based Sensor Fusion for Autonomous Driving: A SurveyCode0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Automotive RADAR sub-sampling via object detection networks: Leveraging prior signal information—0
MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts—0
Causal Explanations for Sequential Decision-Making in Multi-Agent SystemsCode0
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion—0
Credal Bayesian Deep Learning—0
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge—0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving—0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
3M3D: Multi-view, Multi-path, Multi-representation for 3D Object Detection—0
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality MetaversesCode0
Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection—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