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 751–775 of 6092 papers

TitleStatusHype
BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement ModuleCode0
Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous DrivingCode2
GAC-Net_Geometric and attention-based Network for Depth Completion—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—0
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving—0
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process ThinkingCode2
Decoding Interpretable Logic Rules from Neural Networks—0
LEO: Boosting Mixture of Vision Encoders for Multimodal Large Language ModelsCode1
Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving—0
Common Sense Is All You Need—0
Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion—0
TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/VideosCode0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving—0
DriVLM: Domain Adaptation of Vision-Language Models in Autonomous Driving—0
Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions—0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection—0
The global consensus on the risk management of autonomous driving—0
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models—0
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation DataCode0
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection—0
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions—0
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in 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