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 1501–1525 of 6092 papers

TitleStatusHype
P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic SegmentationCode0
Brno Urban Dataset -- The New Data for Self-Driving Agents and Mapping TasksCode0
OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based CamerasCode0
Paint and Distill: Boosting 3D Object Detection with Semantic Passing NetworkCode0
BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement ModuleCode0
Master's Thesis: Out-of-distribution Detection with Energy-based ModelsCode0
Orientation-aware Semantic Segmentation on Icosahedron SpheresCode0
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out ClassifiersCode0
Pair-wise Layer Attention with Spatial Masking for Video PredictionCode0
Parallel Detection for Efficient Video Analytics at the EdgeCode0
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic ScenariosCode0
Brain-Inspired Deep Imitation Learning for Autonomous Driving SystemsCode0
Opendenselane: A Dense Lidar-Based Dataset for HD Map ConstructionCode0
Boundary State Generation for Testing and Improvement of Autonomous Driving SystemsCode0
On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural NetworksCode0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
On the Fly Neural Style Smoothing for Risk-Averse Domain GeneralizationCode0
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial ApplicationsCode0
Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous RacingCode0
An Empirical Bayes Analysis of Object Trajectory Representation ModelsCode0
Boosting Visual Recognition in Real-world Degradations via Unsupervised Feature Enhancement Module with Deep Channel PriorCode0
Online Distillation with Continual Learning for Cyclic Domain ShiftsCode0
Boosting Real-Time Driving Scene Parsing with Shared SemanticsCode0
On Learning Informative Trajectory Embeddings for Imitation, Classification and RegressionCode0
An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutionsCode0
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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