Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/filipcano/safety-shields-delayed
공식 코드https://github.com/fishsoup0/autonomous-driving-perception
공식 코드https://github.com/fiveai/hardest
공식 코드Query-based Hard-Image Retrieval for Object Detection at Test Time
https://github.com/fiveai/hgp_experiments
공식 코드An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
https://github.com/fiveai/saod
공식 코드Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration
https://github.com/fjq-tongji/hcoenet
공식 코드https://github.com/fjq-tongji/mllm-sul
공식 코드https://github.com/flysoaryun/lf-slam
공식 코드https://github.com/flysoaryun/lf-vio
공식 코드LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
https://github.com/flysoaryun/lf-vio-loop
공식 코드https://github.com/flysoaryun/lf-vislam
공식 코드https://github.com/fordcvresearch/fir-trajectory-prediction
공식 코드FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
https://github.com/francescocappio/resend
공식 코드https://github.com/francescosecci/Python_Image_Failures
공식 코드RGB cameras failures and their effects in autonomous driving applications
https://github.com/frankfengdi/LidarMTL
공식 코드A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
https://github.com/frankwnb/collaborative-perception-datasets-for-autonomous-driving
공식 코드Collaborative Perception Datasets for Autonomous Driving: A Review
https://github.com/franpin/topopoint
공식 코드TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
https://github.com/fraunhofer-iais/dlplatform/tree/master/DLplatform
공식 코드Efficient Decentralized Deep Learning by Dynamic Model Averaging
https://github.com/fraunhoferhhi/btseg
공식 코드BTSeg: Barlow Twins Regularization for Domain Adaptation in Semantic Segmentation
https://github.com/freedompuppy77/charon
공식 코드Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space
https://github.com/freeformrobotics/lifelong-monodepth
공식 코드Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
https://github.com/freeformrobotics/progressive-self-distillation-for-ground-to-aerial-perception-knowledge-transfer
공식 코드Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer
https://github.com/ftrojanattack/ftrojan
공식 코드https://github.com/fubel/stmc
공식 코드Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
https://github.com/fudan-projecttitan/openannotate3d
공식 코드OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data
https://github.com/fudan-zvg/bridgead
공식 코드Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
https://github.com/fudan-zvg/deepinteraction
공식 코드DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
https://github.com/fudan-zvg/demo
공식 코드DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States
https://github.com/fudan-zvg/gs-lidar
공식 코드GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting
https://github.com/fudan-zvg/nerf-lidar
공식 코드NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
https://github.com/fudan-zvg/polarformer
공식 코드PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
https://github.com/fudan-zvg/realengine
공식 코드RealEngine: Simulating Autonomous Driving in Realistic Context
https://github.com/fudan-zvg/realmotion
공식 코드https://github.com/fudan-zvg/reason2drive
공식 코드Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving
https://github.com/fudan-zvg/roadnet
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.