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 2051–2075 of 6092 papers

TitleStatusHype
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving—0
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack—0
Benchmarking the Robustness of Semantic Segmentation Models—0
A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of Radar Perception for Autonomous Driving—0
Active-O3: Empowering Multimodal Large Language Models with Active Perception via GRPO—0
DOEPatch: Dynamically Optimized Ensemble Model for Adversarial Patches Generation—0
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving—0
Do-AIQ: A Design-of-Experiment Approach to Quality Evaluation of AI Mislabel Detection Algorithm—0
Active Legibility in Multiagent Reinforcement Learning—0
DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles—0
DMLO: Deep Matching LiDAR Odometry—0
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving—0
DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
3D Vision-Language Gaussian Splatting—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
LAM3D: Leveraging Attention for Monocular 3D Object Detection—0
DLA: Dense-Layer-Analysis for Adversarial Example Detection—0
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving—0
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge—0
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling—0
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
AmodalSynthDrive: A Synthetic Amodal Perception Dataset for Autonomous Driving—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