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/Majiker/STAR

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Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction

https://github.com/ManipulativeConsultant/AutonomousDrivingCookbook

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Adaptive Symmetric Reward Noising for Reinforcement Learning

https://github.com/Manojbhat09/Trajformer

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Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving

https://github.com/MarvinTeichmann/MultiNet

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MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/Mazin-Hnewa/MS-DAYOLO

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Multiscale Domain Adaptive YOLO for Cross-Domain Object Detection

https://github.com/Megvii-BaseDetection/YOLOX

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YOLOX: Exceeding YOLO Series in 2021

https://github.com/MeixinZhu/Velocity_control

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Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

https://github.com/MikeS96/3d_obj_detection

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High-level camera-LiDAR fusion for 3D object detection with machine learning

https://github.com/MoonBlvd/tad-IROS2019

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Unsupervised Traffic Accident Detection in First-Person Videos

https://github.com/MoyGcc/hpcwild

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Human Performance Capture from Monocular Video in the Wild

https://github.com/Mrmoore98/VectorMapNet_code

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VectorMapNet: End-to-end Vectorized HD Map Learning

https://github.com/MushroomHunting/RSS2020-online-domain-adaptation-pot

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Online Domain Adaptation for Occupancy Mapping

https://github.com/NJUVISION/rho-vision

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Efficient Visual Computing with Camera RAW Snapshots

https://github.com/NVlabs/FocalFormer3D

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FocalFormer3D : Focusing on Hard Instance for 3D Object Detection

https://github.com/NeurAI-Lab/G2S

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Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth Estimation

https://github.com/Nightmare-n/UniPAD

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UniPAD: A Universal Pre-training Paradigm for Autonomous Driving

https://github.com/NingMingHao/Yolov8TensorrtRos

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An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion

https://github.com/NomiMalik0207/PedSynth-and-PedGNN-for-Pedestrian-Intention-Prediction

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Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

https://github.com/OPIXray-author/OPIXray

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Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module

https://github.com/OpenDriveLab/DetAny3D

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Detect Anything 3D in the Wild

https://github.com/OpenDriveLab/LaneSegNet

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LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

https://github.com/OpenDriveLab/PersFormer_3DLane

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PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark

https://github.com/OpenPerceptionX/TCP

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Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline

https://github.com/OrangeSodahub/CRLFnet

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RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving

https://github.com/Owen-Liuyuxuan/visualDet3D

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Ground-aware Monocular 3D Object Detection for Autonomous Driving

https://github.com/Ozzyz/carla-data-export

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Multimodal 3D Object Detection from Simulated Pretraining

https://github.com/PDillis/guiding-e2e

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Guiding Attention in End-to-End Driving Models

https://github.com/PJLab-ADG/DiLu

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DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

https://github.com/PJLab-ADG/LimSim

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LimSim: A Long-term Interactive Multi-scenario Traffic Simulator

https://github.com/PJLab-ADG/driveLikeAHuman

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Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

https://github.com/PaddlePaddle/PaddleSeg

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PaddleSeg: A High-Efficient Development Toolkit for Image Segmentation

https://github.com/Peterbotliang/NEBPMOT

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Neural Enhanced Belief Propagation for Multiobject Tracking

https://github.com/PrajwalKoirala/LSPC-Safe-Offline-RL

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Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning

https://github.com/QingyongHu/SoTA-Point-Cloud

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Deep Learning for 3D Point Clouds: A Survey

https://github.com/Qjizhi/GenStereo

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GenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching

https://github.com/RAufschlaeger/cRID

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Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence

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