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 4176–4200 of 6092 papers

TitleStatusHype
Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Survey—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques—0
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection—0
Advancing Auto-Regressive Continuation for Video Frames—0
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap—0
Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation—0
AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion—0
Adversarial Attacks on Monocular Depth Estimation—0
Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving—0
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery—0
Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios—0
Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning—0
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—0
Adversarial Objects Against LiDAR-Based Autonomous Driving Systems—0
Adversarial Prompt Distillation for Vision-Language Models—0
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving—0
Adversarial Vulnerability of Temporal Feature Networks for Object Detection—0
Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
Adverse Weather Conditions Augmentation of LiDAR Scenes with Latent Diffusion Models—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception—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