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 4251–4275 of 6092 papers

TitleStatusHype
A Light-Weight Communication-Efficient Data Sharing Approach in 5G NR V2X—0
A Lightweight Machine Learning Pipeline for LiDAR-simulation—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
A lightweight YOLOv5-FFM model for occlusion pedestrian detection—0
Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence—0
All-optical image classification through unknown random diffusers using a single-pixel diffractive network—0
All-photon Polarimetric Time-of-Flight Imaging—0
ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots—0
AlterMOMA: Fusion Redundancy Pruning for Camera-LiDAR Fusion Models with Alternative Modality Masking—0
A machine learning environment for evaluating autonomous driving software—0
A Machine Learning Framework for Distributed Functional Compression over Wireless Channels in IoT—0
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification—0
Ambiguous Annotations: When is a Pedestrian not a Pedestrian?—0
A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos—0
A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System—0
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving—0
A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving—0
A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment—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