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/sarthaksharma13/IROS18
https://github.com/sarthaksharma13/RenderForCNN_KeypointGeneration
https://github.com/sarthaksharma13/RenderForCNN_annotation
https://github.com/scope-lab-vu/deep-nn-car
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
https://github.com/scope-lab-vu/risk-aware-scene-generation-cps
https://github.com/securade/hub
Parallel Detection for Efficient Video Analytics at the Edge
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/shengyp/fake_news
https://github.com/shihaibi/MV3D
Multi-View 3D Object Detection Network for Autonomous Driving
https://github.com/shin-eunsu/GTA_Autodriving_LogitechG29
Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments
https://github.com/shunchan0677/deepware
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
https://github.com/shyhyawJou/Binarized-Neural-Network
Architecturing Binarized Neural Networks for Traffic Sign Recognition
https://github.com/siat-invs/carlaflcav
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation
https://github.com/siddhanthaldar/PyTorch_Object_Detection
CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving
https://github.com/sisl/InterpretableValidation.jl
https://github.com/sjchoi86/choicenet
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks
https://github.com/smlbansal/deepreach
DeepReach: A Deep Learning Approach to High-Dimensional Reachability
https://github.com/sobhan-moosavi/DCRNN
https://github.com/sobhan-moosavi/characterizingdrivingstyleswithdeeplearning
https://github.com/spirka3/master-cnn
https://github.com/srinu6/Stereo-3D-Object-Detection-for-Autonomous-Driving
Stereo R-CNN based 3D Object Detection for Autonomous Driving
https://github.com/ssg-research/flare
https://github.com/sshirahmad/GCRL
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
https://github.com/stanfordasl/hj_reachability
DeepReach: A Deep Learning Approach to High-Dimensional Reachability
https://github.com/stepankonev/waymo-motion-prediction-challenge-2022-multipath-plus-plus
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction
https://github.com/stevewongv/sparse2dense
https://github.com/supernlogn/squeezeDetTL
https://github.com/syscv/shift-dev
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
https://github.com/taewankim1/uncertainty_deeplearning
https://github.com/talk2car/Talk2Car
https://github.com/tantara/PeerNets-pytorch
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
https://github.com/tcwangshiqi-columbia/Neurify
https://github.com/tcwangshiqi-columbia/ReluVal
https://github.com/tejas-gokhale/AGAT
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.