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/SinaMohseni/Saliency-Based-Failure-prediction-for-Autonomous-Vehicle
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems
https://github.com/SmallMunich/nutonomy_pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/Srijan2001/Keypoint-regression
https://github.com/StanfordVL/STR-PIP
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
https://github.com/StephenStorm/YOLOX
https://github.com/Suryavf/SelfDrivingCar
Exploring the Limitations of Behavior Cloning for Autonomous Driving
https://github.com/SysCV/qd-3dt
https://github.com/THU17cyz/3DIoUMatch-PVRCNN
3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection
https://github.com/TestSelection/TestSelection
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/TraffiX-VideoQA/TUMTraffic-VideoQA-Baseline
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
https://github.com/TrustAI/AdversarialDriving
Adversarial Driving: Attacking End-to-End Autonomous Driving
https://github.com/TsingZ0/PFL-Non-IID
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
https://github.com/Tsinghua-MARS-Lab/HDMapNet
HDMapNet: An Online HD Map Construction and Evaluation Framework
https://github.com/Tsinghua-MARS-Lab/vectormapnet
https://github.com/TuSimple/RangeDet
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
https://github.com/Uuuuuuho/Pillar_on_SoC
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/Vipermdl/E2E-ERFNet
End-to-End Lane Marker Detection via Row-wise Classification
https://github.com/VladimirYugay/MAGiC-SLAM
https://github.com/VladyslavUsenko/basalt-headers-mirror
https://github.com/WaterScenes/Awesome-Water-Surface-Perception
https://github.com/Winaway/Path_planning
https://github.com/WongKinYiu/yolor
2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection
https://github.com/XRONHH/text_1
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
https://github.com/XuLena/3D-sensing
https://github.com/YHKimGithub/FATSnet
Temporally smooth online action detection using cycle-consistent future anticipation
https://github.com/YU1ut/Ensemble-of-Leave-out-Classifiers
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
https://github.com/YXu556/RoadExtraction
Fine-Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation
https://github.com/Yang-YiFan/DiracDeltaNet
https://github.com/YaraAlnaggar/SqueezeSeg
https://github.com/YeLyuUT/UAVidToolKit
https://github.com/YiZhou-H/Trajectory-Mamba-CVPR
Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM
https://github.com/YuanJianhao508/RAG-Driver
https://github.com/YuejiangLIU/social-nce-trajectron-plus-plus
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/Yvanali/KITTISeg
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
https://github.com/ZexinLi0w0/R3
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.