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 1926–1950 of 2605 papers

TitleStatusHype
3D Point Cloud Processing and Learning for Autonomous Driving—0
3D Scene Understanding at Urban Intersection using Stereo Vision and Digital Map—0
Enabling 3D Object Detection with a Low-Resolution LiDAR—0
4C: A Computation, Communication, and Control Co-Design Framework for CAVs—0
4D-MultispectralNet: Multispectral Stereoscopic Disparity Estimation using Human Masks—0
5G enabled Mobile Edge Computing security for Autonomous Vehicles—0
5G Network Slicing: Analysis of Multiple Machine Learning Classifiers—0
6G Cellular Networks and Connected Autonomous Vehicles—0
A 65 nm Bayesian Neural Network Accelerator with 360 fJ/Sample In-Word GRNG for AI Uncertainty Estimation—0
AA3DNet: Attention Augmented Real Time 3D Object Detection—0
A Benchmark for Vision-Centric HD Mapping by V2I Systems—0
A Bird's-eye View of Language Processing Projects at the Romanian Academy—0
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving—0
A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities—0
A Case Study of Image Enhancement Algorithms' Effectiveness of Improving Neural Networks' Performance on Adverse Images—0
A CASE STUDY ON OPTIMAL DEEP LEARNING MODEL FOR UAVS—0
Acceleration method for generating perception failure scenarios based on editing Markov process—0
Accurate Trajectory Prediction for Autonomous Vehicles—0
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search—0
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles—0
A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data—0
A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications—0
A Comprehensive Eco-Driving Strategy for Connected and Autonomous Vehicles (CAVs) with Microscopic Traffic Simulation Testing Evaluation—0
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU—0
A Computational Model of Commonsense Moral Decision Making—0
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Benchmark Results

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