The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the computational complexity of algorithms and their potential to ensure safe operation of automated vehicles are often encountered. Situation-aware environment perception presents one promising example, where computational resources are distributed to regions within the perception area that are relevant for the task of the automated vehicle. While prior map knowledge is often leveraged to identify relevant regions, in this work, we present a lightweight identification of safety-relevant regions that relies solely on online information. We show that our approach enables safe vehicle operation in critical scenarios, while retaining the benefits of non-uniformly distributed resources within the environment perception.

翻译：随着自动化驾驶领域自动化水平的提高,对车辆操作安全的要求也越来越多,计算资源的限制、算法的计算复杂性与其确保自动车辆安全操作的潜力之间的权衡往往会遇到。环境状况认识是一个很有希望的例子,计算资源分布在与自动化车辆任务相关的概念领域内的各个区域。虽然以前对地图的了解常常被用来查明相关区域,但在这项工作中,我们对完全依赖在线信息的安全相关区域进行了轻量化的识别。我们表明,我们的方法能够在关键情况下安全地操作车辆,同时保留环境观念内非统一分配资源的好处。