https://github.com/TixXx1337/Federated-Learning-with-Heterogeneous-Data-Handling-for-Robust-Vehicular-Perception
공식 코드Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection
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/TobyPDE/FRRN
공식 코드Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
https://github.com/Tom-E-Durham/Dur360BEV
공식 코드https://github.com/Tom-E-Durham/icra2025-dur360bev
공식 코드https://github.com/TonyXuQAQ/Topo-boundary
공식 코드iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving
https://github.com/Traffic-X/MonoLSS
공식 코드MonoLSS: Learnable Sample Selection For Monocular 3D Detection
https://github.com/Tsinghua-MARS-Lab/DenseTNT
공식 코드DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution
https://github.com/Tsinghua-MARS-Lab/Occ3D
공식 코드Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
https://github.com/Tsinghua-MARS-Lab/ViP3D
공식 코드ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries
https://github.com/TuSimple/LiDAR_SOT
공식 코드Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
https://github.com/Twizwei/bddoia_project
공식 코드Explainable Object-induced Action Decision for Autonomous Vehicles
https://github.com/USTCAVSA/TSL
공식 코드https://github.com/UTAustin-SwarmLab/NeuS-V
공식 코드Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification
https://github.com/VCCIV/BLVD
공식 코드BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
https://github.com/VDIGPKU/OpenAD
공식 코드OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
https://github.com/VLISLAB/360-DL-Survey
공식 코드Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
https://github.com/VSlabRobotics/PFSD
공식 코드PFSD: A Multi-Modal Pedestrian-Focus Scene Dataset for Rich Tasks in Semi-Structured Environments
https://github.com/ValentinaZangirolami/DRL
공식 코드https://github.com/VerwimpEli/CLAD
공식 코드CLAD: A realistic Continual Learning benchmark for Autonomous Driving
https://github.com/W-zx-Y/DANNet
공식 코드DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
https://github.com/WangLibo1995/GeoSeg
공식 코드https://github.com/WangYueFt/pillar-od
공식 코드https://github.com/WaterScenes/USVTrack-Baseline-YOLO-Tracking
공식 코드https://github.com/Weafre/CNeT
공식 코드https://github.com/WeikaiTan/Toronto-3D
공식 코드Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
https://github.com/Weixy21/ABNet
공식 코드ABNet: Attention BarrierNet for Safe and Scalable Robot Learning
https://github.com/WesleyHsieh0806/TAO-Amodal
공식 코드https://github.com/Willy1005/2025-IJCAI-RDML
공식 코드Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks
https://github.com/WilsonMqz/CoA
공식 코드https://github.com/WoojuLee24/OA-DG
공식 코드https://github.com/Xiangxu-0103/FRNet
공식 코드FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
https://github.com/Yan-Xia/VPC-Net
공식 코드https://github.com/YangZhang4065/AdaptationSeg
공식 코드Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
https://github.com/Yaozhuwa/FisheyeSeg
공식 코드Universal Semantic Segmentation for Fisheye Urban Driving Images
https://github.com/YeLyuUT/MSDNet
공식 코드https://github.com/YingdaXia/SynthCP
공식 코드Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.