Generation and communication of dynamic maps using light projection
Generation and communication of dynamic maps using light projection
复制标题
使用光投影生成动态地图并进行通信
DOI：
10.5194/ica-proc-1-16-2018
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Brenner
中科院分区：
文献类型：
--
作者：
S. Busch;A. Schlichting;C. Brenner
抽象。许多交通事故是由于交通参与者之间的沟通不畅造成的。为了消除这一事故原因，在车对车和车对基础设施通信领域正在进行大量研究。然而，很少有人关注汽车的行为如何传达给行人的问题。特别是考虑到自动化交通，汽车和行人之间缺乏沟通。在本文中，我们解决了自动驾驶汽车如何告知行人其意图的问题。特别是在高度自动化驾驶的情况下，与驾驶员进行眼神接触不会给他或她的意图提供任何线索。我们开发了一种原型，通过将视觉图案投射到地面上，不断向行人告知车辆的意图。此外，该系统将其对观察到的情况的解释传达给行人，以警告他们或鼓励他们执行特定动作。为了进行自适应通信，车辆需要了解城市的动态，以了解在某些情况下会发生什么以及什么速度是合适的。为了支持这一点，我们创建了一个动态地图，它可以估计某个区域的行人和骑自行车的人数，然后用来确定该区域的“危险程度”。该动态地图是从来自许多时间实例的测量数据获得的，这与当今流行的静态汽车导航地图形成对比。除了用于通信目的外，动态地图还可以影响汽车的速度，无论是手动还是自动驾驶。在危险区域调整速度将避免汽车开得太快的事故，这样无论是人类还是计算机操作的系统都无法及时停车。
Abstract. Many accidents are caused by miscommunication between traffic participants. Much research is being conducted in the area of car to car and car to infrastructure communication in order to eliminate this cause of accidents. How-ever, less attention is paid to the question how the behavior of a car can be communicated to pedestrians. Especially considering automated traffic, there is a lack of communication between cars and pedestrians. In this paper, we address the question how an autonomously driving car can inform pedestrians about its intentions. Especially in case of highly automated driving, making eye contact with a driver will give no clue about his or her intensions. We developed a prototype which continuously informs pedestrians about the intentions of the vehicle by projecting visual patterns onto the ground. Furthermore, the system communicates its interpretation of the observed situation to the pedestrians to warn them or to encourage them to perform a certain action. In order to communicate adaptively, the vehicle needs to develop an understanding of the dynamics of a city to know what to expect in certain situations and what speed is appropriate. To support this, we created a dynamic map, which estimates the number of pedestrians and cyclists in a certain area, which is then used to determine how ‘hazardous’ the area is. This dynamic map is obtained from measurement data from many time instances, in contrast to the static car navigation maps, which are prevalent today. Apart from being used for communication purposes, the dynamic map can also influence the speed of a car, be it manually or autonomously driven. Adapting the speed in hazardous areas will avoid accidents where a car drives too fast, so that neither a human nor a computer-operated system would be able to stop in time.