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/EmergenceAI/Agent-E

공식 코드

Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems

https://github.com/EnVision-Research/MotionDreamer

공식 코드

Motion Dreamer: Realizing Physically Coherent Video Generation through Scene-Aware Motion Reasoning

https://github.com/Equinoxxxxx/MulCPred_code

공식 코드

MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction

https://github.com/EricGuo5513/momask-codes

공식 코드

MoMask: Generative Masked Modeling of 3D Human Motions

https://github.com/ErikBrorsson/MVUDA

공식 코드

MVUDA: Unsupervised Domain Adaptation for Multi-view Pedestrian Detection

https://github.com/Eroica-cpp/YouTube-Statistics

공식 코드

YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

https://github.com/Erosinho13/FedDrive

공식 코드

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

https://github.com/EtienneGof/FunCLBM

공식 코드

Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation

https://github.com/EverettRichards/Edge-CAV

공식 코드

Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception

https://github.com/EvoCargo/RaindropsOnWindshield

공식 코드

Raindrops on Windshield: Dataset and Lightweight Gradient-Based Detection Algorithm

https://github.com/Ezharjan/AutoSceneGen

공식 코드

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

https://github.com/FXDevailly/IG-RL

공식 코드

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

https://github.com/FabianAmherd/CNN_ER_FA

공식 코드

Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network

https://github.com/FilippoAleotti/mobilePydnet

공식 코드

Real-time single image depth perception in the wild with handheld devices

https://github.com/Ford/AVData

공식 코드

Ford Multi-AV Seasonal Dataset

https://github.com/ForzaETH/LLMxRobot

공식 코드

Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models

https://github.com/Fredrik00/avod

공식 코드

Multimodal 3D Object Detection from Simulated Pretraining

https://github.com/GUO-W/MultiMotion

공식 코드

Multi-Person Extreme Motion Prediction

https://github.com/GangZhang842/CPGNet

공식 코드

CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation

https://github.com/GerbenBeintema/SS-encoder-video

공식 코드

Non-linear State-space Model Identification from Video Data using Deep Encoders

https://github.com/GilgameshD/ICRA2020-CMTS

공식 코드

CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

https://github.com/Golchoubian/UncertaintyAware_DRL_CrowdNav

공식 코드

Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space

https://github.com/GradientSpaces/LoopSplat

공식 코드

LoopSplat: Loop Closure by Registering 3D Gaussian Splats

https://github.com/GuanRunwei/Achelous

공식 코드

Achelous: A Fast Unified Water-surface Panoptic Perception Framework based on Fusion of Monocular Camera and 4D mmWave Radar

https://github.com/GuanRunwei/Efficient-VRNet

공식 코드

ASY-VRNet: Waterway Panoptic Driving Perception Model based on Asymmetric Fair Fusion of Vision and 4D mmWave Radar

https://github.com/GuidoManni/BodySLAM

공식 코드

BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

https://github.com/HEmile/neurosymbolic-diffusion

공식 코드

Neurosymbolic Diffusion Models

https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN

공식 코드

Stereo R-CNN based 3D Object Detection for Autonomous Driving

https://github.com/HKUST-Aerial-Robotics/VINS-Mono

공식 코드

Online Temporal Calibration for Monocular Visual-Inertial Systems

https://github.com/HKUST-Aerial-Robotics/spatiotemporal_semantic_corridor

공식 코드

Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor

https://github.com/HMRC-AEL/MonoDEVSNet

공식 코드

Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision

https://github.com/HMX2013/Covex-hull-based-fitting-3D-LiDAR-ROS

공식 코드

An Efficient Convex Hull-based Vehicle Pose Estimation Method for 3D LiDAR

https://github.com/HWQuantum/HistNet

공식 코드

Robust super-resolution depth imaging via a multi-feature fusion deep network

https://github.com/HalleyJiang/DiPE

공식 코드

DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos

https://github.com/HanLingsgjk/CSCV

공식 코드

ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video

https://github.com/HanjiangHu/DASGIL

공식 코드

DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization

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