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A Hierarchical System Integration Approach with Application to Visual Scene Exploration for Driver Assistance

机译：应用于驾驶员辅助视觉场景探索的分层系统集成方法

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摘要

A scene exploration which is quick and complete according to current task is the foundation for most higher scene processing. Many specialized approaches exist in the driver assistance domain (e.g. car recognition or lane marking detection), but we aim at an integrated system, combining several such techniques to achieve sufficient performance. In this work we present a novel approach to this integration problem. Algorithms are contained in hierarchically arranged layers with the main principle that the ordering is induced by the requirement that each layer depends only on the layers below. Thus, higher layers can be added to a running system (incremental composition) and shutdown or failure of higher layers leaves the system in an operational state, albeit with reduced functionality (graceful degradation). Assumptions, challenges and benefits when applying this approach to practical systems are discussed. We demonstrate our approach on an integrated system performing visual scene exploration on real-world data from a prototype vehicle. System performance is evaluated on two scene exploration completeness measures and shown to gracefully degrade as several layers are removed and to fully recover as these layers are restarted while the system is running.
机译：根据当前任务快速和完整的场景探索是最高场景处理的基础。驾驶员辅助领域存在许多专业方法（例如汽车识别或车道标记检测），但我们的目标是综合系统，结合了几种这样的技术来实现足够的性能。在这项工作中，我们提出了一种新的融合问题。算法包含在分层布置的层中，主要原理是通过每个层仅取决于下面的层所要求的要求引起的顺序。因此，可以将较高的层添加到运行系统（增量组合物），并且更高层的关断或故障在操作状态下离开系统，尽管具有减少的功能（优雅降级）。讨论了将这种方法应用于实际系统时的假设，挑战和益处。我们展示了我们在从原型车辆对实际数据执行视野探索的集成系统的方法。系统性能在两个场景探索完整性测量上进行评估，并显示为优雅地降低，因为在系统运行时重新启动这些图层时，删除了几层并完全恢复。

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