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/mehilshah/bug_reproducibility_dl_bugs

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Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study

https://github.com/mehmetaygun/4d-pls

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4D Panoptic LiDAR Segmentation

https://github.com/mehooz/vision4leg

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Human-AI Shared Control via Policy Dissection

https://github.com/mehrab-abrar/Sim2Real

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An Open-source Sim2Real Approach for Sensor-independent Robot Navigation in a Grid

https://github.com/meituan/yolov6

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YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

https://github.com/memberre/asyncdriver

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Asynchronous Large Language Model Enhanced Planner for Autonomous Driving

https://github.com/menandro/vfs

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Real-Time Variational Fisheye Stereo without Rectification and Undistortion

https://github.com/mengmengliu1998/LAformer

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LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints

https://github.com/mengmengliu1998/gatraj

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GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model

https://github.com/mengyuanchen21/awesome-evidential-deep-learning

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A Comprehensive Survey on Evidential Deep Learning and Its Applications

https://github.com/metra4ok/hpointloc

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HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images

https://github.com/metro-smiles/ProgGen

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Programmatic Video Prediction Using Large Language Models

https://github.com/mgineer117/meta-cpo

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Constrained Meta-Reinforcement Learning for Adaptable Safety Guarantee with Differentiable Convex Programming

https://github.com/mh0797/interplan

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Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

https://github.com/mhammoud115/da-rl

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Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning

https://github.com/mi-biolab/region-learner

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Region Prediction for Efficient Robot Localization on Large Maps

https://github.com/mi3labucm/coooler

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Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety

https://github.com/mia-cong/swift

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Can Test-Time Scaling Improve World Foundation Model?

https://github.com/miaoqiaowei/awesome-4d

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Advances in 4D Generation: A Survey

https://github.com/miaoxiong2320/neighboragg

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Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation

https://github.com/michael-fonder/M4Depth

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M4Depth: Monocular depth estimation for autonomous vehicles in unseen environments

https://github.com/michael-fonder/m4depthu

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A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles

https://github.com/michaelkoelle/qmarl-evo

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Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization

https://github.com/michealcarac/VSLAM-Mapping

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Autonomous Navigation System from Simultaneous Localization and Mapping

https://github.com/michigan-traffic-lab/DECODE

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DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

https://github.com/michigan-traffic-lab/lightemma

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LightEMMA: Lightweight End-to-End Multimodal Model for Autonomous Driving

https://github.com/mickeykang16/ev3dod

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Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras

https://github.com/microsoft/Magma

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Magma: A Foundation Model for Multimodal AI Agents

https://github.com/miguelag99/efficient-instance-prediction

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Fast and Efficient Transformer-based Method for Bird's Eye View Instance Prediction

https://github.com/mileyan/Pseudo_Lidar_V2

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Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

https://github.com/mileyan/pseudo-LiDAR_e2e

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End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection

https://github.com/mileyan/pseudo_lidar

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Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving

https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege

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Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

https://github.com/mingfeicheng/drivetester

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DriveTester: A Unified Platform for Simulation-Based Autonomous Driving Testing

https://github.com/mingu6/TopometricLoc

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Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness

https://github.com/mingyuliu1/autonomous_driving_datasets

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A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook

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