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/sair-lab/islam

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iSLAM: Imperative SLAM

https://github.com/sajjadmzf/trajpred

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Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks

https://github.com/sakharok13/x-ray-teacher-patching-tools

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Weak-to-Strong 3D Object Detection with X-Ray Distillation

https://github.com/salmank255/ROAD_Waymo_Baseline

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ROAD-Waymo: Action Awareness at Scale for Autonomous Driving

https://github.com/saltoricristiano/cosmix-uda

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CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation

https://github.com/saltoricristiano/gipso-sfouda

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GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation

https://github.com/samedyalcin/mrad

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Impact of Surface Reflections in Maritime Obstacle Detection

https://github.com/sampo-kuutti/adversarial-mixture-density-networks

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Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data

https://github.com/sampo-kuutti/adversarially-robust-control

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ARC: Adversarially Robust Control Policies for Autonomous Vehicles

https://github.com/sampo-kuutti/training-adversarial-agents

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Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies

https://github.com/sampo-kuutti/weakly-supervised-rl-highway-driving

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Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages

https://github.com/sampson-lee/rail-detection

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Rail Detection: An Efficient Row-based Network and A New Benchmark

https://github.com/samsja/finding-a-needle-code

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Improving traffic sign recognition by active search

https://github.com/samuel-cerezo/SLAM-Render

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SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM

https://github.com/samuelmallick/mpcrl-vehicle-gears

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Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection

https://github.com/sancarlim/densetnt-intent

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Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs

https://github.com/sancarlim/explainable-mp

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Towards Explainable Motion Prediction using Heterogeneous Graph Representations

https://github.com/sancharisen/EMPIR

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EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

https://github.com/sander3101/Fast-LiDAR

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Fast LiDAR Upsampling using Conditional Diffusion Models

https://github.com/sanjay-810/aydiv2

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AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer

https://github.com/sanjay-810/qicvt

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Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs

https://github.com/sanjay-810/savid

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SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging Environments

https://github.com/sanjayseshan/autodrive

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Horus: Using Sensor Fusion to Combine Infrastructure and On-board Sensing to Improve Autonomous Vehicle Safety

https://github.com/sanmin0312/P2D

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Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images

https://github.com/sapirkontente/clrmatchnet

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CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

https://github.com/satyajitneogiju/FLDCRF-for-sequence-labeling

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Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields

https://github.com/sbysbysbys/afov

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3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving

https://github.com/sbysbysbys/uov

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3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving

https://github.com/schko/fixatt

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Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertainty

https://github.com/schmidt-ju/argoverse-navmap

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Exploring Navigation Maps for Learning-Based Motion Prediction

https://github.com/schmidt-ju/crat-pred

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CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention

https://github.com/scnu-rislab/cv-mos

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CV-MOS: A Cross-View Model for Motion Segmentation

https://github.com/scnu-rislab/kdmos

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KDMOS:Knowledge Distillation for Motion Segmentation

https://github.com/scnu-rislab/mambaflow

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MambaFlow: A Novel and Flow-guided State Space Model for Scene Flow Estimation

https://github.com/scnu-rislab/mf-mos

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MF-MOS: A Motion-Focused Model for Moving Object Segmentation

https://github.com/scnu-rislab/overlapmamba

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OverlapMamba: Novel Shift State Space Model for LiDAR-based Place Recognition

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