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 3851–3900 of 6092 papers

TitleStatusHype
DreamDrive: Generative 4D Scene Modeling from Street View Images—0
Drive-1-to-3: Enriching Diffusion Priors for Novel View Synthesis of Real Vehicles—0
DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models—0
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models—0
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles—0
DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving—0
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation—0
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation—0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
DriveGPT: Scaling Autoregressive Behavior Models for Driving—0
DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving—0
DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving—0
Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning—0
Driver2vec: Driver Identification from Automotive Data—0
Driver Assistance System Based on Multimodal Data Hazard Detection—0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving—0
DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving—0
DriveScape: High-Resolution Driving Video Generation by Multi-View Feature Fusion—0
DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generation—0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch—0
DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving—0
DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving—0
Driving behavior recognition via self-discovery learning—0
Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map—0
Driving Datasets Literature Review—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Driving Everywhere with Large Language Model Policy Adaptation—0
Driving in Real Life with Inverse Reinforcement Learning—0
Driving in the Occupancy World: Vision-Centric 4D Occupancy Forecasting and Planning via World Models for Autonomous Driving—0
Driving Policy Transfer via Modularity and Abstraction—0
Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications—0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—0
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios—0
Driving Through Ghosts: Behavioral Cloning with False Positives—0
Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples—0
DriVLM: Domain Adaptation of Vision-Language Models in Autonomous Driving—0
DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning—0
DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling—0
Motion Prediction Performance Analysis for Autonomous Driving Systems and the Effects of Tracking Noise—0
D-Score: A White-Box Diagnosis Score for CNNs Based on Mutation Operators—0
DSNet: An Efficient CNN for Road Scene Segmentation—0
DSNet for Real-Time Driving Scene Semantic Segmentation—0
DualAD: Disentangling the Dynamic and Static World for End-to-End Driving—0
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection 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