论文标题
从动态占用网格图中识别威胁区域的情况感知环境感知
Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception
论文作者
论文摘要
自动驾驶领域内自动化更高水平的进步伴随着对车辆操作安全的需求的提高。由计算资源的局限性引起的，算法的计算复杂性之间的权衡及其在确保自动化车辆安全运行的潜力之间经常遇到。情境感知的环境感知提出了一个令人鼓舞的例子，其中计算资源分布在感知区域内的区域，这些区域与自动车辆的任务相关。尽管经常利用先前的地图知识来确定相关区域，但在这项工作中，我们提供了仅依赖在线信息的安全区域的轻量级标识。我们表明，我们的方法可以在关键方案中实现安全的车辆操作，同时在环境感知中保留了非均匀分布资源的好处。
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.
