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/kirk86/RAIOOD

공식 코드

On the Importance of Regularisation & Auxiliary Information in OOD Detection

https://github.com/kit-mrt/future-motion

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JointMotion: Joint Self-Supervision for Joint Motion Prediction

https://github.com/kli58/ROIAL

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Learning Preferences for Interactive Autonomy

https://github.com/konyul/FG-PFE

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Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object Detection

https://github.com/kopp-j/clutter-ds

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Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++

https://github.com/kosieram21/trajflow

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TrajFlow: A Generative Framework for Occupancy Density Estimation Using Normalizing Flows

https://github.com/koyeongmin/PINet

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Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

https://github.com/koyeongmin/PINet_new

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Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

https://github.com/krabs-h/ts-cgnet

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TS-CGNet: Temporal-Spatial Fusion Meets Centerline-Guided Diffusion for BEV Mapping

https://github.com/kshitij3112/evlpsnet

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Uncertainty-aware LiDAR Panoptic Segmentation

https://github.com/ksm26/spatiotemporal-predictions

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Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning

https://github.com/kth-rpl/deflow

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DeFlow: Decoder of Scene Flow Network in Autonomous Driving

https://github.com/kth-rpl/detect_vlm

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Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models

https://github.com/kth-rpl/drivecmd_llm

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Human-Centric Autonomous Systems With LLMs for User Command Reasoning

https://github.com/kth-rpl/rmp

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RMP: A Random Mask Pretrain Framework for Motion Prediction

https://github.com/kth-rpl/seflow

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SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving

https://github.com/kth-rpl/ssf

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SSF: Sparse Long-Range Scene Flow for Autonomous Driving

https://github.com/kuanchihhuang/monodtr

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MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

https://github.com/kujason/avod

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Joint 3D Proposal Generation and Object Detection from View Aggregation

https://github.com/kulikovv/DeepColoring

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Instance Segmentation by Deep Coloring

https://github.com/kumuji/anomaly-benchmark-suite

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OoDIS: Anomaly Instance Segmentation Benchmark

https://github.com/kumuji/ugains

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UGainS: Uncertainty Guided Anomaly Instance Segmentation

https://github.com/kun150kun/eslam-survey

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Event-based Simultaneous Localization and Mapping: A Comprehensive Survey

https://github.com/kwwcv/selfmotion

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Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations

https://github.com/kwwcv/ssmp

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Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix

https://github.com/kyk120/pointfix

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PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation

https://github.com/kztakemoto/mmllm

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The Moral Machine Experiment on Large Language Models

https://github.com/l-scofano/stag

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Staged Contact-Aware Global Human Motion Forecasting

https://github.com/l1997i/DurLAR

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DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications

https://github.com/l1997i/lim3d

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Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

https://github.com/l1997i/rapid_seg

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RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation

https://github.com/lab-of-ai-and-robotics/bayesian_nerf

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Bayesian NeRF: Quantifying Uncertainty with Volume Density for Neural Implicit Fields

https://github.com/lab-of-ai-and-robotics/gs_icp_slam

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RGBD GS-ICP SLAM

https://github.com/lab-sun/amfnet

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Adaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features

https://github.com/lab176344/TrafficScenarios-RFAPsClustering

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Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity

https://github.com/lab176344/Traffic_Sceanrios-VoteBasedEVT

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Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios

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