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 2451–2475 of 2605 papers

TitleStatusHype
Deep Reinforcement Learning for Autonomous Vehicle Intersection Navigation—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles—0
Defensive Perception: Estimation and Monitoring of Neural Network Performance under Deployment—0
Deflating Dataset Bias Using Synthetic Data Augmentation—0
Democratizing Reward Design for Personal and Representative Value-Alignment—0
Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making—0
Dense Road Surface Grip Map Prediction from Multimodal Image Data—0
Dependable Neural Networks for Safety Critical Tasks—0
Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning—0
Depth Estimation using Weighted-loss and Transfer Learning—0
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation—0
Depth Not Needed - An Evaluation of RGB-D Feature Encodings for Off-Road Scene Understanding by Convolutional Neural Network—0
Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs—0
Designing Autonomous Vehicles: Evaluating the Role of Human Emotions and Social Norms—0
Detecting Driveable Area for Autonomous Vehicles—0
Detecting Unsigned Physical Road Incidents from Driver-View Images—0
Detection of Cyber-Attacks in Collaborative Intersection Control—0
Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach—0
Deterministic Guided LiDAR Depth Map Completion—0
Developing Situational Awareness for Joint Action with Autonomous Vehicles—0
Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions—0
Development of Simulation-based Lane Change Control System for Autonomous Vehicles—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement—0
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Benchmark Results

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