Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 4
- 보존된 평가 기록
- 23
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/ChenyanWu/MEBOW
https://github.com/CycloneBoy/PPDetectionPytorch
https://github.com/DIG-Beihang/OPIXray
https://github.com/Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks
https://github.com/DataXujing/YOLOX-
https://github.com/Davidnet/TFSegmentation
https://github.com/Deci-AI/super-gradients
https://github.com/DerrickXuNu/OpenCOOD
https://github.com/Derteanoo/PINet_quantization
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/Dikshuy/SMARTS-lite
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/Discover304/MMDetection3D-Eloss
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/DominikFHG/TensorQuant
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant
https://github.com/Duckkkky/smarts
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/ENSTA-U2IS/MUAD-Dataset
https://github.com/Eisbaer8/DeepGTAV
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
https://github.com/ElementAI/baal
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
https://github.com/Fmohammadsofi/software_defect_predicition_using_LLMs
Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems
https://github.com/GT-RIPL/L2C
https://github.com/GaoangW/TNT
Exploit the Connectivity: Multi-Object Tracking with TrackletNet
https://github.com/Ggs1mida/Awesome-DataFusion
https://github.com/Ggs1mida/Awesome-PointCloudRegistration
https://github.com/GreatV/PP-GuideNet
https://github.com/Guzpenha/slice_based_learning
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
https://github.com/HFAiLab/hdmapnet
HDMapNet: An Online HD Map Construction and Evaluation Framework
https://github.com/Halmstad-University/SalsaNext
https://github.com/Halmstad-University/TITAN-NET
https://github.com/Hanson0910/RetinaTrack
RetinaTrack: Online Single Stage Joint Detection and Tracking
https://github.com/Harlequln/C1M18X-Behavioural_Cloning
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
https://github.com/HussainGhazaly/PINet-master
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/IBM/Autozoom-Attack
https://github.com/IBM/ZOO-Attack
https://github.com/IgorMunizS/mobileDet
https://github.com/IntelLabs/AVUC
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
https://github.com/IvanVassi/LaneNet
LaneNet: Real-Time Lane Detection Networks for Autonomous Driving
https://github.com/JHUVisionLab/multi-modal-regression
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.