The development of fully autonomous vehicles (AVs) can potentially eliminate drivers and introduce unprecedented seating design. However, highly flexible seat configurations may lead to occupants' unconventional poses and actions. Understanding occupant behaviors and prioritize safety features become eye-catching topics in the AV research frontier. Visual sensors have the advantages of cost-efficiency and high-fidelity imaging and become more widely applied for in-car sensing purposes. Occlusion is one big concern for this type of system in crowded car cabins. It is important but largely unknown about how a visual-sensing framework will look like to support 2-D and 3-D human pose tracking towards highly configurable seats. As one of the first studies to touch this topic, we peek into the future camera-based sensing framework via a simulation experiment. Constructed representative car-cabin, seat layouts, and occupant sizes, camera coverage from different angles and positions is simulated and calculated. The comprehensive coverage data are synthesized through an optimization process to determine the camera layout and overall occupant coverage. The results show the needs and design of a different number of cameras to fully or partially cover all the occupants with changeable configurations of up to six seats.
翻译:完全自主的车辆(AV)的开发有可能消除驾驶员,并引入前所未有的座位设计。然而,高度灵活的座位配置可能会导致占住者的非常规姿势和行动。了解占位行为和优先安全特征成为AV研究前沿的吸引视视线的话题。视觉传感器具有成本效益和高不洁成像的优势,并被更广泛地用于汽车遥感目的。隔离是拥挤的汽车机舱中这种类型的系统的一大问题。视觉传感器框架将如何支持2D和3D人的姿势跟踪高度可配置的座椅,这一点很重要,但基本上不为人所知。作为第一次研究,我们通过模拟试验浏览未来基于摄像头的感测框架。建造有代表性的汽车卡宾、座位布局和摄像头尺寸,模拟和计算不同角度和位置的摄像头覆盖。全面覆盖数据通过优化程序合成,以确定摄像头布局和总体覆盖范围。结果显示,需要和设计不同数量的六座椅,可以完全或部分覆盖全部或部分覆盖全部的摄像机群。