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

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Showing 2301–2310 of 2605 papers

TitleStatusHype
Image-Guided Depth Sampling and Reconstruction—0
Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components—0
Estimation of Tire-Road Friction for Road Vehicles: a Time Delay Neural Network Approach—0
Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random FieldsCode0
A Comparative Study of High-Recall Real-Time Semantic Segmentation Based on Swift Factorized NetworkCode0
Importance-Aware Semantic Segmentation with Efficient Pyramidal Context Network for Navigational Assistant SystemsCode0
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and ClassificationCode0
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques—0
SDNet: Semantically Guided Depth Estimation Network—0
Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors—0
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Benchmark Results

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