https://github.com/gjp1203/LIV360SV
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/gloria111/YOLOX_paddle
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmum/Zero-Time-Waste
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/gnns4hri/sngnn1dv2
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
https://github.com/gnns4hri/socnavgym
A Graph Neural Network to Model Disruption in Human-Aware Robot Navigation
https://github.com/godwinrayanc/YOLOv1-Pytorch
https://github.com/gogojjh/ESVO_extension
https://github.com/gogojjh/M-LOAM
MLOD: Awareness of Extrinsic Perturbation in Multi-LiDAR 3D Object Detection for Autonomous Driving
https://github.com/gokulprasadthekkel/pytorch-multi-class-focal-loss
https://github.com/gomboskriszta/yolo
https://github.com/google-deepmind/scalable_agent
https://github.com/google-research/deeplab2
https://github.com/google-research/human-scene-transformer
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/google/flax/tree/master/examples/imagenet
https://github.com/govardhanharish/selfDriving
https://github.com/govvijaycal/confidence_aware_predictions
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
https://github.com/gowriaddepalli/ML_DL_Research_collab_base
Deep Reinforcement Learning framework for Autonomous Driving
https://github.com/gpleiss/limits_of_large_width
https://github.com/greatsharma/DeepLearning-Papers-Implementation
https://github.com/greerviau/GlorifiedCruiseControl
https://github.com/griffinbran/Democratizing-Autonomous-Driving
https://github.com/griffinbran/machines_best_friend
https://github.com/gsool/Microsoft-AirSim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/guagen/yolov3-ultralytics-source
https://github.com/guilherme-pombo/keras_resnext_fpn
https://github.com/gujralsanyam22/LANE-DETECTION-MASTER-2
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/gundeep15/SCD_AI_Project
https://github.com/gunooknam/Code_Study_Yolov3
https://github.com/guoxixu/TrafficFlowTracking
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
https://github.com/guxiaowei1/yolov3--detection-of-rmb-code-region
https://github.com/guybarnahum1/AirSim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/gxytcrc/Semantic-Graph-based--global-Localization
https://github.com/gxytcrc/semantic-histogram-based-global-localization
https://github.com/gyes00205/waymo_tf_object_detection
https://github.com/gymoon10/Instance-Segmentation-with-Discriminative-Loss
Semantic Instance Segmentation with a Discriminative Loss Function
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.