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 2601–2625 of 6092 papers

TitleStatusHype
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks—0
Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach—0
AdaOcc: Adaptive-Resolution Occupancy Prediction—0
BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment—0
DriveGPT: Scaling Autoregressive Behavior Models for Driving—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
Analytical Safety Bounds for Trajectory Following Controllers in Autonomous Vehicles—0
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model—0
DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving—0
DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models—0
Analysis of Truncated Singular Value Decomposition for Koopman Operator-Based Lane Change Model—0
Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion—0
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation—0
BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images—0
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation—0
Analysis of Functional Insufficiencies and Triggering Conditions to Improve the SOTIF of an MPC-based Trajectory Planner—0
DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Driving—0
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers—0
Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection—0
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles—0
Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models—0
BEVHeight++: Toward Robust Visual Centric 3D Object Detection—0
DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models—0
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets—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