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 3101–3150 of 6092 papers

TitleStatusHype
ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision—0
Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition—0
Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems—0
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
RopeBEV: A Multi-Camera Roadside Perception Network in Bird's-Eye-View—0
Rotationally Equivariant 3D Object Detection—0
RowDetr: End-to-End Row Detection Using Polynomials—0
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection—0
RS2AD: End-to-End Autonomous Driving Data Generation from Roadside Sensor Observations—0
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection—0
RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking—0
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving—0
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving—0
RGB-Thermal Infrared Fusion for Robust Depth Estimation in Complex Environments—0
RTI-NMPC for Control of Autonomous Vehicles Using Implicit Discretization Methods—0
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture—0
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts—0
RV-FuseNet: Range View Based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting—0
Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction—0
S^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving—0
S3Net: 3D LiDAR Sparse Semantic Segmentation Network—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
S^3Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data—0
S³Net: Semantic-Aware Self-supervised Depth Estimation with Monocular Videos and Synthetic Data—0
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving—0
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation—0
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles—0
SaDe: Learning Models that Provably Satisfy Domain Constraints—0
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks—0
Safe Adaptive Cruise Control Under Perception Uncertainty: A Deep Ensemble and Conformal Tube Model Predictive Control Approach—0
Safe and Efficient Path Planning under Uncertainty via Deep Collision Probability Fields—0
Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations—0
Safe and Human-Like Autonomous Driving: A Predictor-Corrector Potential Game Approach—0
Safe-by-Construction Autonomous Vehicle Overtaking using Control Barrier Functions and Model Predictive Control—0
SafeCast: Risk-Responsive Motion Forecasting for Autonomous Vehicles—0
Recursively Feasible Chance-constrained Model Predictive Control under Gaussian Mixture Model Uncertainty—0
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning—0
Safe Distributional Reinforcement Learning—0
SafeDrive: A Robust Lane Tracking System for Autonomous and Assisted Driving Under Limited Visibility—0
SafeDrive: Enhancing Lane Appearance for Autonomous and Assisted Driving Under Limited Visibility—0
SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models—0
Safe end-to-end imitation learning for model predictive control—0
Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses—0
Safeguarding Federated Learning-based Road Condition Classification—0
Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change—0
Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving—0
Safe Mission Planning under Dynamical Uncertainties—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