SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself 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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1250 of 2605 papers

TitleStatusHype
Adaptive Target Tracking Using Retrospective Cost Input Estimation—0
DeepPoint: A Deep Learning Model for 3D Reconstruction in Point Clouds via mmWave Radar—0
Deep Occupancy-Predictive Representations for Autonomous Driving—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
A Multicriteria Decision Making Approach to Study the Barriers to the Adoption of Autonomous Vehicles—0
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection—0
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review—0
Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads—0
Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation—0
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review—0
Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint—0
A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles—0
Adaptive Stress Testing for Autonomous Vehicles—0
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS—0
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review—0
Deep learning based infrared small object segmentation: Challenges and future directions—0
Automatic Testing With Reusable Adversarial Agents—0
Deep Learning Based Autonomous Vehicle Super Resolution DOA Estimation for Safety Driving—0
Automatic driving lane change safety prediction model based on LSTM—0
DeepLaser: Practical Fault Attack on Deep Neural Networks—0
Deep Koopman Operator-Informed Safety Command Governor for Autonomous Vehicles—0
Automatic Calibration of Multiple 3D LiDARs in Urban Environments—0
DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios—0
A machine learning environment for evaluating autonomous driving software—0
DeepFault: Fault Localization for Deep Neural Networks—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology—0
DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems—0
Automated Generation of Diverse and Challenging Scenarios for Test and Evaluation of Autonomous Vehicles—0
A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective—0
Deep Camera Pose Regression Using Pseudo-LiDAR—0
Deconvolutional Networks for Point-Cloud Vehicle Detection and Tracking in Driving Scenarios—0
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving—0
Decomposition and Quantification of SOTIF Requirements for Perception Systems of Autonomous Vehicles—0
Decoding CNN based Object Classifier Using Visualization—0
Automated Detecting and Placing Road Objects from Street-level Images—0
Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities—0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning—0
Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook—0
Automated design of error-resilient and hardware-efficient deep neural networks—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network—0
Automated Data Curation Using GPS & NLP to Generate Instruction-Action Pairs for Autonomous Vehicle Vision-Language Navigation Datasets—0
Algorithmic Approaches to Enhance Safety in Autonomous Vehicles: Minimizing Lane Changes and Merging—0
Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon—0
Decision Making for Autonomous Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified