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/chauncygu/safe-reinforcement-learning-baselines

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A Review of Safe Reinforcement Learning: Methods, Theory and Applications

https://github.com/chaytonmin/OFF-Net

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ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

https://github.com/chaytonmin/Off-Road-Freespace-Detection

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ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

https://github.com/chaytonmin/occ-bev

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UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/occupancy-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chaytonmin/uniscene

공식 코드

UniScene: Multi-Camera Unified Pre-training via 3D Scene Reconstruction for Autonomous Driving

https://github.com/chaytonmin/voxel-mae

공식 코드

Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chen289/Visual-GCN

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PSI: A Pedestrian Behavior Dataset for Socially Intelligent Autonomous Car

https://github.com/chenghuang66/s2tnet

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S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving

https://github.com/chengjt1999/mv-mos

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MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation

https://github.com/chengricky/PAL

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Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

https://github.com/chengweialan/desire-gs

공식 코드

DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes

https://github.com/chengzhag/pansplat

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PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting

https://github.com/chenhongyiyang/widthformer

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WidthFormer: Toward Efficient Transformer-based BEV View Transformation

https://github.com/chensiweithu/whales

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WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving

https://github.com/chensy7/efficient-vit

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Compressing Vision Transformers for Low-Resource Visual Learning

https://github.com/chenxin-dlut/seqtrackv2

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Awesome Multi-modal Object Tracking

https://github.com/chenyirui/fastc

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

https://github.com/chenyx09/belief-planning

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Interactive multi-modal motion planning with Branch Model Predictive Control

https://github.com/chequanghuy/TwinLiteNet

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TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

https://github.com/chequanghuy/TwinLiteNetPlus

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TwinLiteNetPlus: A Stronger Model for Real-time Drivable Area and Lane Segmentation

https://github.com/chinitaberrio/m2s-road

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M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data

https://github.com/chisyliu/GnnPmbTracker

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GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

https://github.com/chma1024/densepass

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Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation

https://github.com/chreisinger/ViLGOD

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Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection

https://github.com/christopherbrix/vnncomp2023

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Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/christopherbrix/vnncomp2023_benchmarks

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Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign Recognition

https://github.com/chuduanfeng/charms

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CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving

https://github.com/chufengt/iccv19_attribute

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Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization

https://github.com/chuhengwei/ki_gan

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KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced Multi-Vehicle Trajectory Forecasting at Signalized Intersections

https://github.com/chuwd19/point-cloud-smoothing

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TPC: Transformation-Specific Smoothing for Point Cloud Models

https://github.com/cidanshi/nitedr-nighttime-image-de-raining

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NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes

https://github.com/cing2/limtr

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LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration

https://github.com/cityflow-project/CityFlow

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CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

https://github.com/cjwyx/ph-ddpg-model

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A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control

https://github.com/cjy1992/detect-loc-map

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End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning

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