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 3501–3525 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
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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