Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Mohamed-ElhajAbdou/Self-driving-car

End to End Learning for Self-Driving Cars

https://github.com/MoonBlvd/tad-IROS2019-TBD-

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems

https://github.com/MrHongweiDu/Road_detection

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow

Deep Residual Learning for Image Recognition

https://github.com/Mrjaggu/Self_driving_car

End to End Learning for Self-Driving Cars

https://github.com/Mrxiangli/HopTrack

HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices

https://github.com/MuditShivendra/Traffic_Light_Control

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/NANSHANB/StereoR-CNN

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/NASA-NeMO-Net/NeMO-Net

Deep Residual Learning for Image Recognition

https://github.com/NBvinay/vinb-ghost-car

YOLOv3: An Incremental Improvement

https://github.com/NEUAutonomousDriving408/CIVCAutonomousDriving

YOLOX: Exceeding YOLO Series in 2021

https://github.com/NOHYC/autonomous_driving_car_project

Deep Reinforcement Learning for Autonomous Driving

https://github.com/NVIDIA-AI-IOT/redtail

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

https://github.com/NVIDIA/retinanet-examples

Deep Residual Learning for Image Recognition

https://github.com/NaturalHistoryMuseum/semantic-segmentation

Semantic Instance Segmentation with a Discriminative Loss Function

https://github.com/Naveenking006/AutoPilot

End to End Learning for Self-Driving Cars

https://github.com/NeekhilD/Learning-Competition

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/Nicholasli1995/EgoNet

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/NickLucche/lane-detection

Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks

https://github.com/Nilanjan96/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Noep1997/self-driving-car

End to End Learning for Self-Driving Cars

https://github.com/NovasMax/R3Det-Refined-Single-Stage-Detector-with-Feature-Refinement-for-RO

Focal Loss for Dense Object Detection

https://github.com/NovasMax/yolov3

YOLOv3: An Incremental Improvement

https://github.com/Nuclearstar/End-to-End-Learning-for-Self-Driving-Cars

End to End Learning for Self-Driving Cars

https://github.com/OATML/carsuite

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

https://github.com/OFRIN/Tensorflow_FCOS

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/OFRIN/Tensorflow_RetinaFace

Focal Loss for Dense Object Detection

https://github.com/OlafenwaMoses/Action-Net

Deep Residual Learning for Image Recognition

https://github.com/OlafenwaMoses/FireNET

YOLOv3: An Incremental Improvement

https://github.com/OlafenwaMoses/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/OlafenwaMoses/Traffic-Net

Deep Residual Learning for Image Recognition

https://github.com/Olayemiy/Image-Classification-With-Resnet

Deep Residual Learning for Image Recognition

https://github.com/OpenCv30/Yolov3

YOLOv3: An Incremental Improvement

https://github.com/OpenDriveLab/OpenLane

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

https://github.com/OpenSpaceAI/EC-Depth

EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes

https://github.com/OrangeSodahub/RMMDet

RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.