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 2391–2400 of 2605 papers

TitleStatusHype
Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments—0
A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios—0
Multi-agent estimation and filtering for minimizing team mean-squared error—0
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles—0
Learning Accurate, Comfortable and Human-like Driving—0
Affordance Learning In Direct Perception for Autonomous Driving—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban ScenesCode0
Fast Deep Stereo with 2D Convolutional Processing of Cost SignaturesCode0
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning—0
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Benchmark Results

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