Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, the approach provides safety guarantees in a probabilistic sense, while for a vanishing risk, the formal safety guarantees of the set-based methods are inherited. By exploring all available behavior options, our approach solves decision making and longitudinal trajectory planning in one step. The available behavior options are provided by a formal representation of the situation context, which is also used to reduce calculation efforts. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track fusion, the information is used in parallel. The motion planning scheme is validated through real-world experiments.
翻译:城市十字路口的移动规划考虑到情况背景,处理隔离问题,并处理测量和预测不确定性是城市自动化驾驶道路上的一大挑战。在这项工作中,我们以抽样优化方法应对这一挑战。为此,我们制定了最佳控制问题,优化低风险和高乘客舒适度。风险的计算依据的是感知信息和使用风险模型的相关不确定性。风险模型结合了基于设定的方法和概率方法。因此,该方法从概率的角度提供了安全保障,而对于风险的消失而言,基于设定方法的正式安全保障是继承下来的。通过探索所有可用的行为选项,我们的方法解决了决策和纵向轨迹规划的一步。可用行为选项是通过正式描述情况来提供的,这也用于减少计算努力。通过外部对基础设施定位传感器的感知,解决了排斥问题。然而,除了将外部和自我认知与轨迹至轨联,信息是同时使用的。运动规划计划通过真实世界的实验得到验证。