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/zerosansan/ros-navigation-mobile-robot

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An implementation of ROS Autonomous Navigation on Parallax Eddie platform

https://github.com/zgojcic/Rigid3DSceneFlow

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Weakly Supervised Learning of Rigid 3D Scene Flow

https://github.com/zhang-tao-whu/DVIS

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DVIS: Decoupled Video Instance Segmentation Framework

https://github.com/zhang-zengjie/code_2023_iecon_shaping_wu

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Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving

https://github.com/zhang-zengjie/intentaware

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Intention-Aware Control Based on Belief-Space Specifications and Stochastic Expansion

https://github.com/zhanggang001/hednet

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HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds

https://github.com/zhangganlin/gloire-slam

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GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM

https://github.com/zhanggongjie/mapvr

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Online Map Vectorization for Autonomous Driving: A Rasterization Perspective

https://github.com/zhanghm1995/forge_vfm4ad

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Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities

https://github.com/zhangsn-19/pan

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Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks

https://github.com/zhangtravis/hist-da

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Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features

https://github.com/zhangyong-tang/gmmt

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Awesome Multi-modal Object Tracking

https://github.com/zhangyp15/beverse

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BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

https://github.com/zhangyp15/graphad

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GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving

https://github.com/zhangyp15/occformer

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OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction

https://github.com/zhangyp15/simmod

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A Simple Baseline for Multi-Camera 3D Object Detection

https://github.com/zhangyushan3/diffsf

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DiffSF: Diffusion Models for Scene Flow Estimation

https://github.com/zhanwei-z/g2ltraj

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G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction

https://github.com/zhao-lab/kalidindi_dpgp_multi_vehicle_2019

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Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field

https://github.com/zhaoweicai/mscnn

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A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

https://github.com/zhefan-xu/cerlab-uav-autonomy

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A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

https://github.com/zhejz/carla-roach

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End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

https://github.com/zhejz/hptr

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Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding

https://github.com/zhejz/trafficbots

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TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction

https://github.com/zhengxi-git/phase-slam

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Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems

https://github.com/zhenzhenxiang/SemanticsSharing

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Boosting Real-Time Driving Scene Parsing with Shared Semantics

https://github.com/zhicongsun/d4dgs-slam

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Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM

https://github.com/zhijian11/DriveMM

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DriveMM: All-in-One Large Multimodal Model for Autonomous Driving

https://github.com/zhijing-jin/3D_Traffic_in_Unity

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3D Traffic Simulation for Autonomous Vehicles in Unity and Python

https://github.com/zhiqi-li/BEVFormer

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BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

https://github.com/zhiqic/DAMO-StreamNet

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DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

https://github.com/zhiqic/longshortnet

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LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception

https://github.com/zhiyuanli218/segret

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SegRet: An Efficient Design for Semantic Segmentation with Retentive Network

https://github.com/zhonghuayi/focusflow_official

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FocusFlow: Boosting Key-Points Optical Flow Estimation for Autonomous Driving

https://github.com/zhonghuayi/optical_flow_robustness_benchmark

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Benchmarking the Robustness of Optical Flow Estimation to Corruptions

https://github.com/zhouhao0605/signal_control_paper

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Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control

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