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：向更高水平自动化驾驶领域的发展伴随着对车辆运行安全性日益增长的要求。受计算资源限制，常需在算法计算复杂度与其确保自动驾驶车辆安全运行的能力之间进行权衡。态势感知环境感知是一个颇具前景的示例，它将计算资源分配至对自动驾驶车辆任务相关的感知区域内。尽管现有地图知识常被用于识别相关区域，但本研究提出了一种仅依赖在线信息的轻量级安全相关区域识别方法。我们证明，该方法在关键场景中能够保障车辆安全运行，同时保持环境感知中非均匀资源分配的优势。