Within the field of automated driving, a clear trend in environment perception tends towards more sensors, higher redundancy, and overall increase in computational power. This is mainly driven by the paradigm to perceive the entire environment as best as possible at all times. However, due to the ongoing rise in functional complexity, compromises have to be considered to ensure real-time capabilities of the perception system. In this work, we introduce a concept for situation-aware environment perception to control the resource allocation towards processing relevant areas within the data as well as towards employing only a subset of functional modules for environment perception, if sufficient for the current driving task. Specifically, we propose to evaluate the context of an automated vehicle to derive a multi-layer attention map (MLAM) that defines relevant areas. Using this MLAM, the optimum of active functional modules is dynamically configured and intra-module processing of only relevant data is enforced. We outline the feasibility of application of our concept using real-world data in a straight-forward implementation for our system at hand. While retaining overall functionality, we achieve a reduction of accumulated processing time of 59%.

翻译：在自动化驾驶领域,环境认知的明显趋势趋向于增加传感器、增加冗余和总体计算功率的增加,这主要是由在任何时候都尽可能最好地看待整个环境的范式驱动的,然而,由于功能复杂性不断提高,必须考虑妥协,以确保认知系统的实时能力。在这项工作中,我们引入了一种环境认知概念,以控制在数据内处理相关领域的资源分配,并仅使用一组功能模块来进行环境认知,如果对目前的驱动任务足够的话。具体地说,我们提议评价一个自动工具的背景,以获得多层关注地图(MLAM),界定相关领域。利用这一MLAM,积极功能模块的优化是动态配置的,并且只对相关数据进行模块内部处理。我们概述了在直接实施系统时使用真实世界数据的可行性。我们保留了总体功能,同时将累积的处理时间缩短了59%。