We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task requires a huge amount of real-world data or a realistic simulator. Using a roundabout scenario, we show that this requirement can be relaxed in favour of targeted, simplistic simulated data. A benefit is that such data can be easily generated for critical scenarios that are typically underrepresented in realistic datasets. By applying vanilla behavioural cloning almost exclusively to lightweight simulated data, we achieve reliable and comfortable real-world driving. Our key insight lies in an incremental development approach that includes regular in-vehicle testing to identify sim-to-real gaps, targeted data augmentation, and training scenario variations. In addition to the methodology, we offer practical guidelines for deploying such a policy within a real-world vehicle, along with insights of the resulting qualitative driving behaviour. This approach serves as a blueprint for many automated driving use cases, providing valuable insights for future research and helping develop efficient and effective solutions.

翻译：我们挑战了普遍认知，即应用深度学习解决自动驾驶规划任务需要海量真实世界数据或逼真的模拟器。通过环形交叉口场景，我们证明这一要求可被放宽，转而采用有针对性的简化模拟数据。其优势在于：这类数据能轻松生成真实数据集中典型缺失的临界场景。通过主要对轻量级模拟数据应用朴素行为克隆，我们实现了可靠且舒适的实车驾驶。核心洞察在于渐进式开发方法，包括定期车载测试以识别模拟到现实的差距、针对性数据增强及训练场景变体。除方法论外，我们提供了在真实车辆中部署此类策略的实践指南，以及所得定性驾驶行为的见解。该方案可作为众多自动驾驶用例的蓝本，为未来研究提供宝贵经验，助力开发高效且有效的解决方案。