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Autonomous Vehicles

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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/antoinedemathelin/openood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/antonoeschmidt/darknet

YOLOv3: An Incremental Improvement

https://github.com/anuragranj/flowattack

Attacking Optical Flow

https://github.com/aotumanbiu/awesome-object-detection

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/apolloauto/apollo-model-yolox

YOLOX: Exceeding YOLO Series in 2021

https://github.com/apostovan21/vnncomp2023

Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/araffin/learning-to-drive-in-5-minutes

Learning to Drive in a Day

https://github.com/arahosu/musicslots

Deep Residual Learning for Image Recognition

https://github.com/arashjavan/deeplio

PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

https://github.com/aravindmanoharan/Self-Driving-Car-V1

End to End Learning for Self-Driving Cars

https://github.com/aravindmanoharan/Self-Driving-Car-V2

End to End Learning for Self-Driving Cars

https://github.com/areberoto/darknet-thesis

YOLOv3: An Incremental Improvement

https://github.com/argoverse/argoverse-api

Argoverse: 3D Tracking and Forecasting with Rich Maps

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/arief25ramadhan/udacity-behavioral-cloning

End to End Learning for Self-Driving Cars

https://github.com/arlo0o/uniscene-unified-occupancy-centric-driving-scene-generation

UniScene: Unified Occupancy-centric Driving Scene Generation

https://github.com/arnavdodiedo/DenseNet-Fashion-MNIST

Densely Connected Convolutional Networks

https://github.com/arnavdodiedo/DenseNet-MNIST

Densely Connected Convolutional Networks

https://github.com/arryyao/flow-project-flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/artemmavrin/focal-loss

Focal Loss for Dense Object Detection

https://github.com/artxtech/darknet-rnn

Focal Loss for Dense Object Detection

https://github.com/artynet/darknet-alexeyAB

Focal Loss for Dense Object Detection

https://github.com/as1067/PiCar

End to End Learning for Self-Driving Cars

https://github.com/as791/Adversarial-Example-Attack-and-Defense

Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

https://github.com/as791/ZOO_Attack_PyTorch

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/asad-62/IVP-DNN

Densely Connected Convolutional Networks

https://github.com/asarav/MIT-Deep-Traffic-Solution

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/aseslamian/Det-SLAM

Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2

https://github.com/asharakeh/kitti_native_evaluation

Joint 3D Proposal Generation and Object Detection from View Aggregation

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/ashtawy/deeptraffic

DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation

https://github.com/ashutosh1997/Self_Driving_Car

End to End Learning for Self-Driving Cars

https://github.com/ashutoshkumar19/Self_Driving_Car

End to End Learning for Self-Driving Cars

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

https://github.com/astoycos/Mini_Project2

Deep Residual Learning for Image Recognition

https://github.com/asvath/mobile_robotics

nuScenes: A multimodal dataset for autonomous driving

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.