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 2026–2050 of 6092 papers

TitleStatusHype
MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object Detection—0
MonoSOWA: Scalable monocular 3D Object detector Without human Annotations—0
On Learning Informative Trajectory Embeddings for Imitation, Classification and RegressionCode0
Distilling Multi-modal Large Language Models for Autonomous Driving—0
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning—0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection—0
BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement ModuleCode0
Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving—0
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition—0
GAC-Net_Geometric and attention-based Network for Depth Completion—0
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—0
Decoding Interpretable Logic Rules from Neural Networks—0
Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving—0
Common Sense Is All You Need—0
TB-Bench: Training and Testing Multi-Modal AI for Understanding Spatio-Temporal Traffic Behaviors from Dashcam Images/VideosCode0
Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
DriVLM: Domain Adaptation of Vision-Language Models in Autonomous Driving—0
Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions—0
CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving—0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection—0
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models—0
The global consensus on the risk management of autonomous driving—0
NextStop: An Improved Tracker For Panoptic LIDAR Segmentation DataCode0
FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection—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