Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Autonomous driving is the task of driving a vehicle 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: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code5/tree/main/SDNet
https://github.com/MindSpore-scientific-2/code-5/tree/main/Integrated-Gradient
https://github.com/MindSpore-scientific/code-11/tree/main/Uncertainty_Calibration_Object_Detection
Uncertainty Calibration and its Application to Object Detection
https://github.com/ModelBunker/Stereo-RCNN-PyTorch
Stereo R-CNN based 3D Object Detection for Autonomous Driving
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/Mrxiangli/HopTrack
HopTrack: A Real-time Multi-Object Tracking System for Embedded Devices
https://github.com/NANSHANB/StereoR-CNN
Stereo R-CNN based 3D Object Detection for Autonomous Driving
https://github.com/NEUAutonomousDriving408/CIVCAutonomousDriving
https://github.com/NOHYC/autonomous_driving_car_project
https://github.com/NeekhilD/Learning-Competition
https://github.com/Nicholasli1995/EgoNet
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection
https://github.com/OpenDriveLab/OpenLane
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
https://github.com/OpenSpaceAI/EC-Depth
https://github.com/OrangeSodahub/RMMDet
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
https://github.com/PINTO0309/PINTO_model_zoo/tree/main/326_YOLOPv2
YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception
https://github.com/PaddlePaddle/Paddle3D
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PeterBeben/continuous-K-SVD
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/PhilippeW83440/CarND-Path-Planning-Project
https://github.com/PointsCoder/ONCE_Benchmark
https://github.com/RenzKa/simlingo
https://github.com/Ricardozzf/sdsrcnn
Illuminating Pedestrians via Simultaneous Detection & Segmentation
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4
https://github.com/Robotmurlock/TNT-VectorNet-and-HOME-Trajectory-Forecasting
https://github.com/SHITIANYU-hue/Efficient-motion-planning
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
https://github.com/SaifeddineBarkia/Multi-view-radar-semantic-segmentation-for-scene-understanding-in-autonomous-driving
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
https://github.com/Santara/RAIL
https://github.com/SensorsINI/ddd20-utils
https://github.com/ShunyuYao/blitznet_instance_segment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.