用仿真训练的视觉模型，让小型自动驾驶车在无定位信号时也能稳走车道。
A Sim-to-Real Vision-based Lane Keeping System for a 1:10-scale Autonomous Vehicle
- 用仿真图像训练小模型，实时估算车辆转向偏差
- 在实验室环境实现无定位信号下的稳定车道保持
- 适合做微型自动驾驶系统开发或教学演示
近年来多项赛事凸显了在感知、建模和定位功能不足场景下，基于视觉解决方案的重要性。本文介绍由DEI-Unipd团队在博世未来出行挑战赛2022中开发的视觉车道保持系统（VbLKS）。主要贡献在于一种面向1:10比例自动驾驶车辆的仿真到现实（Sim2Real）GPS缺失环境下的视觉车道保持系统。该系统采用定制化的纯追踪（PP）控制策略，以恒定前瞻距离估算前视航向误差（LHE）作为输入，通过卷积神经网络（CNN）实现。提出了一种紧凑型CNN的训练策略，重点利用3D Gazebo仿真器生成与增强摄像头图像数据，可在低功耗硬件上实现实时运行。设计了带有微分项的定制化纯追踪横向控制器及基于纯追踪的速度参考生成机制，并通过系统性的时间延迟稳定性分析确定了调参范围。在典型受控实验室内环境进行了验证。
原文摘要 · Abstract (English)
In recent years, several competitions have highlighted the need to investigate vision-based solutions to address scenarios with functional insufficiencies in perception, world modeling and localization. This article presents the Vision-based Lane Keeping System (VbLKS) developed by the DEI-Unipd Team within the context of the Bosch Future Mobility Challenge 2022. The main contribution lies in a Simulation-to-Reality (Sim2Real) GPS-denied VbLKS for a 1:10-scale autonomous vehicle. In this VbLKS, the input to a tailored Pure Pursuit (PP) based control strategy, namely the Lookahead Heading Error (LHE), is estimated at a constant lookahead distance employing a Convolutional Neural Network (CNN). A training strategy for a compact CNN is proposed, emphasizing data generation and augmentation on simulated camera images from a 3D Gazebo simulator, and enabling real-time operation on low-level hardware. A tailored PP-based lateral controller equipped with a derivative action and a PP-based velocity reference generation are implemented. Tuning ranges are established through a systematic time-delay stability analysis. Validation in a representative controlled laboratory setting is provided.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。