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/Radar-Camera-Fusion/Awesome-Radar-Camera-Fusion

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Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review

https://github.com/Radar-Camera-Fusion/Awesome-Radar-Perception

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Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review

https://github.com/RoboticExplorationLab/ALGAMES.jl

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ALGAMES: A Fast Solver for Constrained Dynamic Games

https://github.com/RoboticExplorationLab/LUCIDGames.jl

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LUCIDGames: onLine UnsCented Inverse Dynamic Games forAdaptive Trajectory Prediction and Planning

https://github.com/RoboticsBUT/Brno-Urban-Dataset

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Brno Urban Dataset -- The New Data for Self-Driving Agents and Mapping Tasks

https://github.com/RobustStabilityGuaranteeRL/RobustStabilityGuaranteeRL

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$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee

https://github.com/Rock-100/MonoDet

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Geometry-based Distance Decomposition for Monocular 3D Object Detection

https://github.com/RomanFoell/DRGP-VSS

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Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation

https://github.com/RuijieZhu94/EC-Depth

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EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes

https://github.com/SCP-CN-001/trajectory_dataset_support

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Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making

https://github.com/SHI-Labs/3D-Point-Cloud-Learning

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Deep Learning for 3D Point Cloud Understanding: A Survey

https://github.com/SRainGit/CAE-LO

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CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

https://github.com/SeasonDepth/SeasonDepth

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SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

https://github.com/SehwanChoi0307/Mask2Map

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Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation Masks

https://github.com/Sekeh-Lab/FogGuard

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FogGuard: guarding YOLO against fog using perceptual loss

https://github.com/Shathe/3D-MiniNet

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3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

https://github.com/ShiQiu0419/attentions_in_3D_detection

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Investigating Attention Mechanism in 3D Point Cloud Object Detection

https://github.com/Shorya-Sharma/Semantic-Segmentation-for-Urban-Scene-Images

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Semantic Segmentation for Urban-Scene Images

https://github.com/Simula-COMPLEX/pure

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Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving

https://github.com/Sirui-Xu/DuMMF

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Stochastic Multi-Person 3D Motion Forecasting

https://github.com/Stanford-ILIAD/CARLO

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Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving

https://github.com/StanfordASL/MATS

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MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control

https://github.com/StanfordASL/Trajectron

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The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

https://github.com/StanfordASL/Trajectron-plus-plus

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Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

https://github.com/StiphyJay/MVCTrack

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MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues

https://github.com/SysCV/bdd100k-models

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BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/SysCV/idisc

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iDisc: Internal Discretization for Monocular Depth Estimation

https://github.com/TJ-IPLab/DNet

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Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications

https://github.com/TRAILab/ST-SLidR

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Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss

https://github.com/TUM-AAS/motron-cvpr22

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Motron: Multimodal Probabilistic Human Motion Forecasting

https://github.com/TUM-AVS/DualAD

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DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving

https://github.com/TUMFTM/orbslam-map-saving-extension

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Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension

https://github.com/TeCSAR-UNCC/Pishgu

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Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems

https://github.com/Terascale-All-sensing-Research-Studio/Forecasting_for_Authentication

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Using Motion Forecasting for Behavior-Based Virtual Reality (VR) Authentication

https://github.com/Thinklab-SJTU/Bench2Drive

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Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving

https://github.com/TiagoCortinhal/SalsaNext

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SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

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