Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 1
- 보존된 평가 기록
- 2
Autonomous vehicles is the task of making a vehicle that can guide itself 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: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/securade/hub
Parallel Detection for Efficient Video Analytics at the Edge
https://github.com/segev-shlomov/ST-WebAgentBench
ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
https://github.com/seishinkikuchi/test
https://github.com/sejong-rcv/2021.Paper.TransDSSL
https://github.com/sejong-rcv/INSANet
https://github.com/sem6-nu/AI-PROJECT
https://github.com/sen-uni-kn/specrepair
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks
https://github.com/sennykim/airsim_for_car_and_multirotor
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/sentient-engineering/agent-q
Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
https://github.com/sepuhopar/ReportDD-darknet
https://github.com/sergiosonline/transfercv
https://github.com/serre-lab/gala_tpu
https://github.com/seu-zxj/llm-augmented-mtr
Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving
https://github.com/seungwonoh5/Universal_Autonomous_Detection
Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shanky1947/YOLOv3-Darknet-Custom-Object-Detection
https://github.com/shao-chi/ImageCaption
https://github.com/shaojiawei07/tocom-tem
https://github.com/shashigharti/secure-privateai-scholarship-challenge
https://github.com/shengyp/fake_news
https://github.com/shichaoy/cuboid_detect
https://github.com/shichaoy/matlab_cuboid_detect
https://github.com/shihaibi/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shikorab/tf-faster-rcnn
https://github.com/shin-eunsu/GTA_Autodriving_LogitechG29
Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments
https://github.com/shivendrashahi/Final_Project_Traffic_Sign
Traffic Sign Classification Using Deep Inception Based Convolutional Networks
https://github.com/shkim3220/Microsoft_Airsim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/shleecs/Copy-and-Paste-Networks-for-Deep-Video-Inpainting
https://github.com/shoaibahmed/CrossLayerPooling
https://github.com/shoaibahmed/TSViz-Core
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
https://github.com/shoji9x9/CIFAR-10-By-small-ResNet
https://github.com/shoji9x9/Fashion-MNIST-By-ResNet
https://github.com/shreyas269/Pytorch-defensive-distillation
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.