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 3551–3600 of 6092 papers

TitleStatusHype
Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale—0
Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification—0
Online LiDAR-Camera Extrinsic Parameters Self-checkingCode1
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNsCode1
Learning Preferences for Interactive AutonomyCode0
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey—0
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving—0
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training—0
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method—0
Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection—0
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving—0
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural NetworksCode1
Row-wise LiDAR Lane Detection Network with Lane Correlation RefinementCode1
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving—0
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection—0
Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework—0
AttTrack: Online Deep Attention Transfer for Multi-object Tracking—0
Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge SystemsCode1
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds—0
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving—0
Instance Segmentation with Cross-Modal Consistency—0
Model-Based Imitation Learning for Urban DrivingCode2
An Efficient FPGA Accelerator for Point Cloud—0
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds—0
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object DetectionCode1
Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving—0
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation—0
Learning with Style: Continual Semantic Segmentation Across Tasks and Domains—0
Dimensionality of datasets in object detection networksCode0
Common Corruption Robustness of Point Cloud Detectors: Benchmark and Enhancement—0
Hierarchical Instance Mixing across Domains in Aerial Segmentation—0
BEV-LaneDet: a Simple and Effective 3D Lane Detection BaselineCode0
A Cooperative Perception System Robust to Localization Errors—0
LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLADCode1
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task ConsistencyCode0
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds—0
Uncertainty-aware LiDAR Panoptic SegmentationCode0
Edge Device Deployment of Multi-Tasking Network for Self-Driving Operations—0
Optimal Control for Platooning in Vehicular Networks—0
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap—0
Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model—0
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial ViewpointsCode1
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning DatasetsCode1
Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling—0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene FlowCode0
CLAD: A realistic Continual Learning benchmark for Autonomous DrivingCode1
Cross-Modality Domain Adaptation for Freespace Detection: A Simple yet Effective Baseline—0
Unsupervised confidence for LiDAR depth maps and applicationsCode1
An Active Learning Reliability Method for Systems with Partially Defined Performance FunctionsCode0
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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