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 3876–3900 of 6092 papers

TitleStatusHype
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