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/StanfordASL/Trajectron-plus-plus

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

https://github.com/StanfordASL/unbalanced-opf-toolkit

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On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks -- An Optimal Power Flow Approach

https://github.com/StanfordVL/JRMOT_ROS

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JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments

https://github.com/StiphyJay/MVCTrack

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

https://github.com/SunLoveSheep/Sem-LSD

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Sem-LSD: A Learning-based Semantic Line Segment Detector

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/CaDDN

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Categorical Depth Distribution Network for Monocular 3D Object Detection

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

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

https://github.com/TRI-ML/DDAD

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3D Packing for Self-Supervised Monocular Depth Estimation

https://github.com/TRI-ML/KP3D

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Self-Supervised 3D Keypoint Learning for Ego-motion Estimation

https://github.com/TRI-ML/packnet-sfm

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3D Packing for Self-Supervised Monocular Depth Estimation

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/TUM-CPS-HN/SafeLLMRA

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Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis

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/TalwalkarLab/leaf

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LEAF: A Benchmark for Federated Settings

https://github.com/TaoWangzj/CorrAdaptor

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CorrAdaptor: Adaptive Local Context Learning for Correspondence Pruning

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

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CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

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/TencentYoutuResearch/PedestrianDetection-NohNMS

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NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination

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/ThierryDeruyttere/Talk2Car-Expr

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Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?

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

https://github.com/Tinker-Twins/Robust_Behavioral_Cloning

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Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning

https://github.com/TixXx1337/Federated-Learning-with-Heterogeneous-Data-Handling-for-Robust-Vehicular-Perception

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Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection

https://github.com/TobyPDE/FRRN

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Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

https://github.com/Tom-E-Durham/Dur360BEV

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Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving

https://github.com/Tom-E-Durham/icra2025-dur360bev

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Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving

https://github.com/TonyXuQAQ/Topo-boundary

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iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving

https://github.com/ToyotaResearchInstitute/packnet-sfm

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3D Packing for Self-Supervised Monocular Depth Estimation

https://github.com/Traffic-Alpha/CCDA-Light

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Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies

https://github.com/Traffic-X/MonoLSS

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MonoLSS: Learnable Sample Selection For Monocular 3D Detection

https://github.com/TrustAI/DeepGame

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A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

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