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/xiaomiaozhang/Co-MTP

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Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving

https://github.com/xichennn/msma

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MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration

https://github.com/xieyuser/semi-sd

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Semi-SD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous Driving

https://github.com/xincoder/GRIP

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GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving

https://github.com/xingjunm/awesome-large-model-safety

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Safety at Scale: A Comprehensive Survey of Large Model Safety

https://github.com/xingp/dgfnet

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Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network

https://github.com/xingshuaihuang/dtlight

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Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach

https://github.com/xingyumex/alen

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ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement

https://github.com/xinliu20/graphcspn_eccv2022

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GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs

https://github.com/xinshuoweng/AB3DMOT

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3D Multi-Object Tracking: A Baseline and New Evaluation Metrics

https://github.com/xinyu-laura/mmuda

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Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning

https://github.com/xinyuelxy/bias-behind-the-wheel-fairness-testing-of-autonomous-driving-systems

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Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems

https://github.com/xinzhuma/3dodi-survey

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3D Object Detection from Images for Autonomous Driving: A Survey

https://github.com/xinzhuma/monodle

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Delving into Localization Errors for Monocular 3D Object Detection

https://github.com/xjixzz/vadepth-net

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Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

https://github.com/xjixzz/van_depth

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Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving

https://github.com/xjtlu-cvlab/3drom

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3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization

https://github.com/xjtu-cs-gao/satforhdmap

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Complementing Onboard Sensors with Satellite Map: A New Perspective for HD Map Construction

https://github.com/xjtu-haolin/point_assisted_sample_selection

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Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection

https://github.com/xl-sr/CAL

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Conditional Affordance Learning for Driving in Urban Environments

https://github.com/xmed-lab/nuinstruct

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Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models

https://github.com/xmm-prio/bevtrack

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BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View

https://github.com/xr-lee/neural-symbolic

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Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction

https://github.com/xrenaa/Deblur-by-Fitting

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Video Deblurring by Fitting to Test Data

https://github.com/xrenaa/safety-aware-motion-prediction

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Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving

https://github.com/xtinkt/editable

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Editable Neural Networks

https://github.com/xu2729/cosdh

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

https://github.com/xuefengbupt/indoorobstaclediscovery-rg

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Indoor Obstacle Discovery on Reflective Ground via Monocular Camera

https://github.com/xueleichen/snn-dvs-carla

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Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection

https://github.com/xuez-phd/tfdet

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TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection

https://github.com/xupei0610/contextvae

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Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction

https://github.com/xuyangbai/transfusion

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TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

https://github.com/xwf199/parformer

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PARFormer: Transformer-based Multi-Task Network for Pedestrian Attribute Recognition

https://github.com/xy-guo/GwcNet

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Group-wise Correlation Stereo Network

https://github.com/xz-group/AdverseDrive

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Attacking Vision-based Perception in End-to-End Autonomous Driving Models

https://github.com/yahidar/radarformer

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RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model

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