发表机构
Federal University of Santa Catarina (UFSC)（圣卡塔琳娜联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出了一种微型阿克曼车辆端到端自动驾驶的低成本开放平台，实现命令条件式行为克隆，经仿真与真实实验验证，可完成赛道行驶，为仿真到真实研究提供测试平台并发布以支持可复现研究。
AI 中文摘要
本文提出了一种用于微型阿克曼车辆端到端自动驾驶研究的低成本开放实验平台。该平台结合了物理车辆、印刷城市赛道、数据采集工具、轨迹配准和Webots数字孪生，可开展受控实验，将基于仿真的自动驾驶方法与实际执行相连接。作为首个基线，我们实现了命令条件式行为克隆，其中神经策略接收车载摄像头图像和高级导航命令，输出转向和速度。该系统在物理车辆和仿真中均进行了评估。在实际闭环实验中，学习到的策略可沿车道行驶并执行命令转向，相对于参考路线的平均横向偏差为6.1厘米，接近人类演示的4.7厘米。在数字孪生中，摄像头视场角对性能有显著影响，从58度扩大至120度时，平均横向偏差从35.6厘米降至3.3厘米。利用数字孪生生成合成驾驶数据，并通过学习到的仿真到真实图像转换器缩小外观差距，我们进一步表明，在该合成数据与真实演示结合上训练的更高容量策略，是唯一能在闭环中完成全部四条赛道路线的配置，而紧凑基线和仅在真实数据上训练的同一网络完成的路线更少。这些结果表明，该开放平台是用于仿真到真实研究的实用测试平台，并提供了初始的命令条件式模仿学习基线；我们发布该平台以支持可复现研究。
英文摘要
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles. The platform combines a physical vehicle, a printed urban track, data collection tools, trajectory registration, and a Webots digital twin, enabling controlled experiments that connect simulation-based autonomous-driving methods to real-world execution. As a first baseline, we implement command-conditioned behavior cloning, in which a neural policy receives an on-board camera image and a high-level navigation command and outputs steering and speed. The system is evaluated both on the physical vehicle and in simulation. In real closed-loop experiments, the learned policy follows lanes and executes commanded turns, reaching a mean cross-track error of 6.1 cm with respect to the reference route, close to the 4.7 cm observed in human demonstrations. In the digital twin, camera field of view has a strong effect on performance, reducing the mean cross-track error from 35.6 to 3.3 cm when widened from 58 to 120 degrees. Using the digital twin to generate synthetic driving data and a learned sim-to-real image translator to reduce the appearance gap, we further show that a higher-capacity policy trained on this synthetic data combined with real demonstrations is the only configuration that completes all four track routes in closed loop, whereas the compact baseline and the same network trained on real data alone complete fewer. These results establish the open platform as a practical testbed for sim-to-real studies and provide an initial command-conditioned imitation-learning baseline; we release it to support reproducible research.
