Ensuring safe behavior for automated vehicles in unregulated traffic areas poses a complex challenge for the industry. It is an open problem to provide scalable and certifiable solutions to this challenge. We derive a trajectory planner based on model predictive control which interoperates with a monitoring system for pedestrian safety based on cellular automata. The combined planner-monitor system is demonstrated on the example of a narrow indoor parking environment. The system features deterministic behavior, mitigating the immanent risk of black boxes and offering full certifiability. By using fundamental and conservative prediction models of pedestrians the monitor is able to determine a safe drivable area in the partially occluded and unstructured parking environment. The information is fed to the trajectory planner which ensures the vehicle remains in the safe drivable area at any time through constrained optimization. We show how the approach enables solving plenty of situations in tight parking garage scenarios. Even though conservative prediction models are applied, evaluations indicate a performant system for the tested low-speed navigation.

翻译：在无管制交通区确保自动车辆的安全行为对业界来说是一个复杂的挑战。 提供可扩缩和可验证的应对这一挑战的办法是一个公开的问题。 我们根据模型预测控制得出一个轨迹规划仪,该轨迹规划仪与基于蜂窝自动式汽车的行人安全监测系统互相操作。 综合规划员-监测仪系统在狭窄的室内停车环境的例子中展示。 该系统具有确定性的行为特征,减轻黑盒的固有风险并提供完全的可验证性。 通过使用基本和保守的行人预测模型,监测仪能够确定部分隐蔽和无结构的停车场环境中的安全可驾驶区。 信息被输入轨迹规划仪,该轨迹规划仪通过节制优化确保车辆随时留在安全的可驾驶区。 我们展示了该方法如何在紧凑的停车场情景下解决大量情况。 尽管采用了保守的预测模型,但评价显示测试的低速导航的性能系统。