We present our approach for the development, validation and deployment of a data-driven decision-making function for the automated control of a vehicle. The decisionmaking function, based on an artificial neural network is trained to steer the mobile robot SPIDER towards a predefined, static path to a target point while avoiding collisions with obstacles along the path. The training is conducted by means of proximal policy optimisation (PPO), a state of the art algorithm from the field of reinforcement learning. The resulting controller is validated using KPIs quantifying its capability to follow a given path and its reactivity on perceived obstacles along the path. The corresponding tests are carried out in the training environment. Additionally, the tests shall be performed as well in the robotics situation Gazebo and in real world scenarios. For the latter the controller is deployed on a FPGA-based development platform, the FRACTAL platform, and integrated into the SPIDER software stack.

翻译：本文介绍了用于车辆自动化控制的数据驱动决策功能的开发、验证与部署方法。该决策功能基于人工神经网络训练，使移动机器人SPIDER能够沿预定义的静态路径向目标点行驶，同时在路径中避开障碍物。训练采用强化学习领域的最先进算法——近端策略优化（PPO）。通过量化控制器跟踪给定路径的能力及对路径上感知障碍物的反应性指标（KPI）进行验证，相关测试在训练环境中完成。此外，测试还将在机器人仿真环境Gazebo及真实场景中执行。针对真实场景，控制器部署于基于FPGA的开发平台FRACTAL上，并集成至SPIDER软件栈中。