Papers with Code 아카이브

Autonomous Racing

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CPS-TUWien/racing_dreamer

공식 코드

Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing

https://github.com/CSL-KU/TinyLidarNet

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TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing

https://github.com/CadenzaCoda/ConstraintAwareIL

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A Simple Approach to Constraint-Aware Imitation Learning with Application to Autonomous Racing

https://github.com/Trustworthy-Engineered-Autonomy-Lab/generalizable-image-repair

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Generalizable Image Repair for Robust Visual Autonomous Racing

https://github.com/albertgassol1/coat_mpc

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Performance-driven Constrained Optimal Auto-Tuner for MPC

https://github.com/albertgassol1/coatmpc

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Performance-driven Constrained Optimal Auto-Tuner for MPC

https://github.com/cv-core/MIT-Driverless-CV-TrainingInfra

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Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions

https://github.com/dasgringuen/assetto_corsa_gym

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A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

https://github.com/forzaeth/rlpp

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RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms

https://github.com/hieutrong512/Yoloz

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YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

https://github.com/hynkis/MIHO

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Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

https://github.com/hynkis/mi-hpo

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Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems

https://github.com/jainachin/bayesrace

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BayesRace: Learning to race autonomously using prior experience

https://github.com/learn-to-race/l2r

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Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

https://github.com/linklab-uva/deepracing

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DeepRacing: Parameterized Trajectories for Autonomous Racing

https://github.com/m4d-sc1entist/mega-dagger

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MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

https://github.com/prishita123/curriculum_rl_for_driving

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Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning

https://github.com/qut-motorsport/qutms_nav_integration

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Racing With ROS 2 A Navigation System for an Autonomous Formula Student Race Car

https://github.com/raphajaner/rpl4f110

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Residual Policy Learning for Vehicle Control of Autonomous Racing Cars

https://github.com/shuoyang2000/neural_hybrid_cbf

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Learning Local Control Barrier Functions for Hybrid Systems

https://github.com/sisl/latentimportancesampling

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Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows

https://github.com/suwesh/parallel-perception-network

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Parallel Neural Computing for Scene Understanding from LiDAR Perception in Autonomous Racing

https://github.com/tinker-twins/autorace-simulator

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Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture

https://github.com/travelbureau/f0_icml_code

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FormulaZero: Distributionally Robust Online Adaptation via Offline Population Synthesis

https://github.com/dvij542/offtersim

Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

https://github.com/nqt228/YoloZ

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

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