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/JeremyBYU/OrganizedPointFilters
공식 코드https://github.com/JeremyBYU/polylidar
공식 코드https://github.com/Jiaolong/trajectory-prediction
공식 코드Trajectory Prediction for Autonomous Driving with Topometric Map
https://github.com/JiaxiLi1/OWLed
공식 코드OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework
https://github.com/JingyuanZhou/Safe_RL_Mixed_Traffic
공식 코드Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning
https://github.com/Jinming-Su/SGNet
공식 코드https://github.com/JoseLGomez/Co-training_SemSeg_UDA
공식 코드Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
https://github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories
공식 코드https://github.com/Jpvmello/traffic-light-detection-synthetic-context
공식 코드Deep traffic light detection by overlaying synthetic context on arbitrary natural images
https://github.com/JudasDie/SOTS
공식 코드https://github.com/JulianoLagana/MT3
공식 코드Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
https://github.com/Jumponthemoon/WeatherDG
공식 코드https://github.com/Jumponthemoon/WeatherEdit
공식 코드WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
https://github.com/JunweiLiang/Multiverse
공식 코드The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
https://github.com/KarlXing/LUSR
공식 코드Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
https://github.com/KenYu910645/perspective-aware-convolution
공식 코드Perspective-aware Convolution for Monocular 3D Object Detection
https://github.com/Kguo-cs/iPad
공식 코드iPad: Iterative Proposal-centric End-to-End Autonomous Driving
https://github.com/Khrylx/AgentFormer
공식 코드AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
https://github.com/Kin-Zhang/mmfn
공식 코드https://github.com/Kthyeon/ssfod
공식 코드Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
https://github.com/Kun-k/Re_2_H2O
공식 코드https://github.com/Kzernobog/small_obstacle_discovery
공식 코드https://github.com/LCAD-UFES/publications-tabelini-ijcnn-2019
공식 코드Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
https://github.com/LTTM/LSR
공식 코드Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation
https://github.com/Lavreniuk/SPIdepth
공식 코드SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation
https://github.com/LincanLi98/Awesome-Data-Centric-Autonomous-Driving
공식 코드https://github.com/LinxiFan/SECANT
공식 코드SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
https://github.com/LucaHermes/lightweight-motion-forecasting
공식 코드Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting
https://github.com/Lukeeeeee/baconian-project
공식 코드Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
https://github.com/Luodian/MADAN
공식 코드https://github.com/MILAB-IIT-CV/ATDN_vSLAM
공식 코드https://github.com/MLAI-Yonsei/CaRot
공식 코드Towards Calibrated Robust Fine-Tuning of Vision-Language Models
https://github.com/MSiam/TFSegmentation
공식 코드https://github.com/MSiam/video_class_agnostic_segmentation
공식 코드Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
https://github.com/MajidMoghadam2006/deepcars-reinforcement-learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.