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

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Showing 4151–4200 of 6092 papers

TitleStatusHype
AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments—0
A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios—0
A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets—0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
AdaWM: Adaptive World Model based Planning for Autonomous Driving—0
ADCNet: Learning from Raw Radar Data via Distillation—0
ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving—0
Addressing Action Oscillations through Learning Policy Inertia—0
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation—0
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning—0
AD-EE: Early Exiting for Fast and Reliable Vision-Language Models in Autonomous Driving—0
A Deep Learning Framework for Boundary-Aware Semantic Segmentation—0
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories—0
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset—0
ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems—0
ADriver-I: A General World Model for Autonomous Driving—0
A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN—0
A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving—0
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems—0
Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF—0
Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving—0
Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze—0
Advanced Object Detection and Pose Estimation with Hybrid Task Cascade and High-Resolution Networks—0
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