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/zqyu9/lusnar-dataset

공식 코드

LuSNAR:A Lunar Segmentation, Navigation and Reconstruction Dataset based on Muti-sensor for Autonomous Exploration

https://github.com/zqzqz/advcollaborativeperception

공식 코드

On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures

https://github.com/zrfreya/AutoRemover

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AutoRemover: Automatic Object Removal for Autonomous Driving Videos

https://github.com/zrrskywalker/monodetr

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MonoDETR: Depth-guided Transformer for Monocular 3D Object Detection

https://github.com/ztsrxh/roadbev

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RoadBEV: Road Surface Reconstruction in Bird's Eye View

https://github.com/zubair-irshad/Awesome-Implicit-NeRF-Robotics

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Neural Fields in Robotics: A Survey

https://github.com/zuosc19/gaussianworld

공식 코드

GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction

https://github.com/zuosc19/quadricformer

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QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction

https://github.com/zuzuba/CISR_NeurIPS20

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Safe Reinforcement Learning via Curriculum Induction

https://github.com/zya3d/awesome-3d-occupancy-prediction

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Vision-based 3D occupancy prediction in autonomous driving: a review and outlook

https://github.com/zyangchen/mocha-stereo

공식 코드

Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph

https://github.com/zympsyche/bevworld

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BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space

https://github.com/zyr17/unilight

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Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

https://github.com/zzj403/BEV_Robust

공식 코드

Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving

https://github.com/zzy-zhou/dsec-mos

공식 코드

Event-Free Moving Object Segmentation from Moving Ego Vehicle

https://github.com/zzy-zhou/renet

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RGB-Event Fusion for Moving Object Detection in Autonomous Driving

https://gitlab.au.dk/maleci/opendr/ua-ab3dmot

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Uncertainty-Aware AB3DMOT by Variational 3D Object Detection

https://gitlab.au.dk/maleci/opendr/variational-voxel-3d-detetction

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Variational Voxel Pseudo Image Tracking

https://gitlab.au.dk/maleci/opendr/vnn_vpit_opendr

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Variational Voxel Pseudo Image Tracking

https://gitlab.com/Atanukundu/FlexiFal

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Data-Driven Falsification of Cyber-Physical Systems

https://gitlab.com/aksoyeren/salsanet

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SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving

https://gitlab.com/haghdam/deep_active_learning

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Active Learning for Deep Detection Neural Networks

https://gitlab.com/peroyose/radial_prediction_layers

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Radial Prediction Layer

https://gitlab.com/sumo-rl/sumo_openai_gym

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Urban Driving with Multi-Objective Deep Reinforcement Learning

https://gitlab.com/sustainable-continuum-monitoring/self-adaptive-moop

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An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge

https://gitlab.com/wenlu057/traffic-safety

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SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

https://zenodo.org/record/12611978

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MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception

https://zenodo.org/record/6619279

공식 코드

Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering

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