本文提出一种低成本、开源的实验平台，用于微型阿克曼车辆端到端自主驾驶的研究。该平台整合了实体车辆、印刷城市赛道、数据采集工具、轨迹配准模块以及 Webots 数字孪生系统，支持可控实验，从而将基于仿真的自主驾驶方法与真实世界执行相连接。作为首个基线方案，我们实现了指令条件化的行为克隆（command-conditioned behavior cloning）：神经策略网络接收车载摄像头图像及高层导航指令，并输出转向角与速度。该系统在实体车辆与仿真环境中均进行了评估。在真实闭环实验中，所学习策略可沿车道行驶并执行指令转向，其相对于参考路径的平均横向误差为 6.1 cm，接近人类演示中观察到的 4.7 cm。在数字孪生环境中，相机视场角对性能影响显著：当视场角从 58 度扩大至 120 度时，平均横向误差由 35.6 cm 降至 3.3 cm。进一步地，我们利用数字孪生生成合成驾驶数据，并采用学习所得的仿真到真实图像翻译器缩小外观差异；结果表明，仅当采用该合成数据与真实演示联合训练的高容量策略时，系统可在闭环中成功完成全部四条赛道路线；而紧凑型基线模型及仅用真实数据训练的同构网络则无法完成全部路线。这些结果确立了该开源平台作为仿真到真实（sim-to-real）研究实用测试平台的地位，并提供了首个指令条件化的模仿学习基线；我们已公开发布该平台以支持可复现研究。

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.