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 3501–3550 of 6092 papers

TitleStatusHype
Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery—0
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment—0
Conditioning Latent-Space Clusters for Real-World Anomaly Classification—0
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation—0
Confidence-based federated distillation for vision-based lane-centering—0
Confidence Calibration for Object Detection and Segmentation—0
Confidence Guided Stereo 3D Object Detection with Split Depth Estimation—0
Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems—0
Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts—0
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes—0
Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors—0
Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection—0
Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms—0
Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data—0
Context-Aware Scene Prediction Network (CASPNet)—0
Context-Aware Semantic Segmentation: Enhancing Pixel-Level Understanding with Large Language Models for Advanced Vision Applications—0
Context-Hierarchy Inverse Reinforcement Learning—0
Contextual road lane and symbol generation for autonomous driving—0
ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models—0
Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments—0
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy—0
Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction—0
Continual Vision-based Reinforcement Learning with Group Symmetries—0
Continual Road-Scene Semantic Segmentation via Feature-Aligned Symmetric Multi-Modal Network—0
ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation—0
Continuous Safety Verification of Neural Networks—0
Contract-based hierarchical control using predictive feasibility value functions—0
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents—0
Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation—0
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds—0
Control-Aware Prediction Objectives for Autonomous Driving—0
Control Informed Design of the IAC Autonomous Racecar for Operation at the Dynamic Envelope—0
Control Invariant Sets for Neural Network Dynamical Systems and Recursive Feasibility in Model Predictive Control—0
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries—0
ControlLoc: Physical-World Hijacking Attack on Visual Perception in Autonomous Driving—0
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving—0
Convolutional Unscented Kalman Filter for Multi-Object Tracking with Outliers—0
Co-Occ: Coupling Explicit Feature Fusion with Volume Rendering Regularization for Multi-Modal 3D Semantic Occupancy Prediction—0
Cooperative Driving for Speed Harmonization in Mixed-Traffic Environments—0
Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles—0
Cooperative Perception with Learning-Based V2V communications—0
Cooperative Visual-LiDAR Extrinsic Calibration Technology for Intersection Vehicle-Infrastructure: A review—0
Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication—0
CoPEM: Cooperative Perception Error Models for Autonomous Driving—0
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches—0
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks—0
CorrBEV: Multi-View 3D Object Detection by Correlation Learning with Multi-modal Prototypes—0
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection—0
Correlation Pyramid Network for 3D Single Object Tracking—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