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Self-Driving Cars

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Self-driving cars : the task of making a car that can drive itself without human guidance.

<span style="color:grey; opacity: 0.6">( Image credit: Learning a Driving Simulator )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Abhaya1998/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/AeroZealous4/Self_Driving_Car_CNN

End to End Learning for Self-Driving Cars

https://github.com/AhmadWaly/Endtoend-learning-for-self-driving-cars

End to End Learning for Self-Driving Cars

https://github.com/AlexeyZhuravlev/visual-backprop

VisualBackProp: efficient visualization of CNNs

https://github.com/AmrElsersy/PointPainting

PointPainting: Sequential Fusion for 3D Object Detection

https://github.com/Andreas-Pfeuffer/LSTM-ICNet

Separable Convolutional LSTMs for Faster Video Segmentation

https://github.com/ArasStar/comma_ai_speed_challenge2017_Tensorflow

End to End Learning for Self-Driving Cars

https://github.com/Ashafix/NeuroMario

End to End Learning for Self-Driving Cars

https://github.com/BrianYang2013/JHU_AI_Journey

Why AI is Harder Than We Think

https://github.com/CoderHun/VectorNet_Implement

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/DQSSSSS/VectorNet

VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation

https://github.com/Dekki-Aero/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Eisbaer8/DeepGTAV

Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

https://github.com/ElSchuer/e2e_lane_keeping

End to End Learning for Self-Driving Cars

https://github.com/FabrizioPuzzo/CarND-Behavioral-Cloning-P4

End to End Learning for Self-Driving Cars

https://github.com/FabrizioPuzzo/End-to-End-Learning

End to End Learning for Self-Driving Cars

https://github.com/GK4444/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Gabriel-Macias/robust_frcnn

A Robust Learning Approach to Domain Adaptive Object Detection

https://github.com/Garvit-32/Automating-Steering-Wheel-Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Garvit-32/Self_Driving_Car_Using_Keras

End to End Learning for Self-Driving Cars

https://github.com/GeondoPark/attribute-preserve

Attribution Preservation in Network Compression for Reliable Network Interpretation

https://github.com/Git10Shikhar/Self-Driving-Cars

End to End Learning for Self-Driving Cars

https://github.com/Git10Shikhar/Self-Driving-Cars-Steering-Controller

End to End Learning for Self-Driving Cars

https://github.com/Harlequln/C1M18X-Behavioural_Cloning

End to End Learning for Self-Driving Cars

https://github.com/HarshaVardhanVanama/Autopilot-Steering-Control

End to End Learning for Self-Driving Cars

https://github.com/HarshaVardhanVanama/Autopilot-Steering-Wheel-Simulation

End to End Learning for Self-Driving Cars

https://github.com/Hp990/Self-driving-car

End to End Learning for Self-Driving Cars

https://github.com/JdeRobot/BehaviorSuite

An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset

https://github.com/JdeRobot/DL-Studio

End to End Learning for Self-Driving Cars

https://github.com/JdeRobot/DeepLearningStudio

End to End Learning for Self-Driving Cars

https://github.com/JdeRobot/NeuralBehaviors

An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset

https://github.com/Jiang-Muyun/Open3D-Semantic-KITTI-vis

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

https://github.com/JiuSenso/SELD-TCN

SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

https://github.com/KatyaKom/Python-Z3-NNVerif

Safety Verification of Deep Neural Networks

https://github.com/M155K4R4/autopilot

End to End Learning for Self-Driving Cars

https://github.com/MS-Mind/MS-Code-06/tree/main/OctSqueeze

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

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