Efficient Resource Allocation for Attentive Automotive Vision Systems
Efficient Resource Allocation for Attentive Automotive Vision Systems
复制标题
专心汽车视觉系统的高效资源分配
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Y. Pétillot
中科院分区：
文献类型：
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作者：
S. Matzka;A. Wallace;Y. Pétillot
我们描述了一种新颖的汽车视觉架构，该架构按五个抽象级别组织，分别是传感器、数据、语义、推理和资源分配级别。尽管我们实施和评估过程来检测和分类移动车辆的直接环境中的其他参与者，但我们的主要重点是计算资源的分配和传感器套件的细心处理。为此，一种有效的多目标资源分配方法被形式化并实施。这包括取决于环境、当前目标、可用传感器和计算资源以及可用于做出决策的时间的决策过程。我们通过奥迪提供的测试车辆获取的道路交通测试序列来评估我们的方法。除了传统的全球定位系统（GPS）导航之外，该车辆还包括激光雷达、视频、雷达和声纳传感器，但我们的评估仅限于激光雷达和视频数据。
We describe a novel architecture for automotive vision organized on five levels of abstraction, i.e., sensor, data, semantic, reasoning, and resource allocation levels, respectively. Although we implement and evaluate processes to detect and classify other participants within the immediate environment of a moving vehicle, our main emphasis is on the allocation of computational resource and attentive processing by the sensor suite. To that end, an efficient multiobjective resource allocation method is formalized and implemented. This includes a decision-making process dependent upon the environment, the current goal, the available sensors and computational resource, and the time available to make a decision. We evaluate our approach on road traffic test sequences acquired by a test vehicle provided by Audi. This vehicle includes lidar, video, radar, and sonar sensors, in addition to conventional global positioning system (GPS) navigation, but our evaluation is confined to lidar and video data alone.