https://github.com/2023-MindSpore-1/ms-code-172
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/2023-MindSpore-1/ms-code-18/tree/main/lenet
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
https://github.com/2023-MindSpore-1/ms-code-182
https://github.com/2023-MindSpore-1/ms-code-191
https://github.com/2023-MindSpore-1/ms-code-194
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/SDNet
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_yolov3
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/TNT
https://github.com/2023-MindSpore-1/ms-code-220/tree/main/SRC
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
https://github.com/2023-MindSpore-1/ms-code-31
https://github.com/2023-MindSpore-4/Code1/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/Efficientnet
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/2023-MindSpore-4/Code10/tree/main/YOLOX
https://github.com/2023-MindSpore-4/Code11/tree/main/SDNet
https://github.com/2023-MindSpore-4/Code2/tree/main/darknet53
https://github.com/62subinh/deeprl_safety_specification
https://github.com/AI-liu/Complex-YOLO
https://github.com/AICPS/roadscene2vec
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
https://github.com/AIasd/ADFuzz
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
https://github.com/ASGuard-UCI/MSF-ADV
https://github.com/Abhaya1998/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/Adelaide-AI-Group/G2D
https://github.com/Aizierjiang/mr
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
https://github.com/AjJordy/squeezeDet-master
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection
Robust and Efficient Post-Processing for Video Object Detection (REPP)
https://github.com/AllenPeng0209/deeproute_competition
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/AngeLouCN/CFPNet
CFPNet: Channel-wise Feature Pyramid for Real-Time Semantic Segmentation
https://github.com/ArashJavan/PointSeg
PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud
https://github.com/Ashish-Tripathy/AI_SelfDriving
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
https://github.com/AshishMehtaIO/MTLfD-CARLA
Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision
https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot
https://github.com/BerkeleyLearnVerify/VerifAI
https://github.com/Bhaney44/MIT_DeepTraffic
https://github.com/BichenWuUCB/SqueezeSeg
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.