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

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Showing 3351–3400 of 6092 papers

TitleStatusHype
Street-View Image Generation from a Bird's-Eye View LayoutCode1
ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation—0
InstaGraM: Instance-level Graph Modeling for Vectorized HD Map LearningCode0
Deep Planar Parallax for Monocular Depth Estimation—0
Planning and Tracking Control of Full Drive-by-Wire Electric Vehicles in Unstructured Scenario—0
CyberLoc: Towards Accurate Long-term Visual Localization—0
Super Sparse 3D Object Detection—0
Policy Pre-training for Autonomous Driving via Self-supervised Geometric ModelingCode1
Procedural Humans for Computer Vision—0
Benchmarking the Robustness of LiDAR Semantic Segmentation ModelsCode2
Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's-Eye ViewCode1
INT2: Interactive Trajectory Prediction at IntersectionsCode1
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction—0
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow EstimationCode0
Plausible Uncertainties for Human Pose Regression—0
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer—0
Learning Human Dynamics in Autonomous Driving Scenarios—0
TransIFF: An Instance-Level Feature Fusion Framework for Vehicle-Infrastructure Cooperative 3D Detection with Transformers—0
FocalFormer3D: Focusing on Hard Instance for 3D Object DetectionCode2
Graphics2RAW: Mapping Computer Graphics Images to Sensor RAW Images—0
Dionysus: Recovering Scene Structures by Dividing Into Semantic Pieces—0
Single Domain Generalization for LiDAR Semantic SegmentationCode1
Pruning Parameterization With Bi-Level Optimization for Efficient Semantic Segmentation on the Edge—0
Azimuth Super-Resolution for FMCW Radar in Autonomous DrivingCode1
SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial ExamplesCode1
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous DrivingCode1
BEV-LaneDet: An Efficient 3D Lane Detection Based on Virtual Camera via Key-PointsCode0
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving—0
Deep Depth Estimation From Thermal ImageCode0
BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation—0
Query-Centric Trajectory PredictionCode2
Toward RAW Object Detection: A New Benchmark and a New Model—0
BEV-Guided Multi-Modality Fusion for Driving Perception—0
TIPI: Test Time Adaptation With Transformation InvarianceCode1
Benchmarking Robustness of 3D Object Detection to Common CorruptionsCode1
BEV@DC: Bird's-Eye View Assisted Training for Depth Completion—0
Unsupervised 3D Point Cloud Representation Learning by Triangle Constrained Contrast for Autonomous Driving—0
Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving—0
On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer—0
Detachable Novel Views Synthesis of Dynamic Scenes Using Distribution-Driven Neural Radiance FieldsCode1
4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions—0
New Challenges in Reinforcement Learning: A Survey of Security and Privacy—0
Autonomous Driving Simulator based on Neurorobotics Platform—0
A Benchmark Generator for Combinatorial Testing—0
On Transforming Reinforcement Learning by Transformer: The Development Trajectory—0
PanDepth: Joint Panoptic Segmentation and Depth CompletionCode1
Quality at the Tail of Machine Learning Inference—0
A Novel Method for Lane-change Maneuver in Urban Driving Using Predictive Markov Decision Process—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