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AI-based light distribution optimization for adaptive control of automotive headlamps in road traffic

AI-based light distribution optimization for adaptive control of automotive headlamps in road traffic
基于人工智能的光分布优化,用于道路交通中汽车前照灯的自适应控制
批准号:
450942921
负责人:
Professor Dr.-Ing. Tran Quoc Khanh
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
--
资助国家:
德国
项目状态:
未结题
起止时间:

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中文摘要
翻译
该研究项目涉及基于人工智能的汽车灯光分布控制的开发,以生成情景优化的动态灯光分布。该研究项目的目的是打破传统的将光分布划分为近光和远光的做法,并呈现整体的光分布。为了开发这些光分布,将进行广泛的研究,以记录当前的交通区域。这里,将首先记录德国交通区域的代表性数据。计划驾驶的目的是记录整个德国交通区域,考虑到与真实情况相似的不同道路等级。对于评估,对象识别的最新算法被单独训练,并创建了不同对象的几何分布,汽车、卡车、公交车(每个驾驶和停车)、交通标志(取决于类别)、行人、骑自行车的人。根据这些对象分布数据以及其他记录的数据,如速度、道路等级、路况等,自动创建不同的交通情况。除了记录德国交通区域外,还在不同道路等级中进行对比调查和亮度分析。这些数据用于确定机动车驾驶员的安全相关灯光要求,并记录德国交通区域的当前灯光状况。不仅考虑了可识别距离和必要的对比度,而且还考虑了前景照明的亮度和均匀性。这是至关重要的,因为除了客观上提高安全性外,主观的安全性感知也对司机的幸福感和行为有很大的影响。这些研究的结果也被纳入到与情境相关的光分布的优化中,此外,进一步的研究记录了驾驶员的注视行为,并结合物体分布和道路交通中的注视行为计算了优化的光分布,验证了这些理论光分布。在第一步骤中,在各种虚拟生成的交通情况下,独立于所使用的技术,在驾驶模拟器中显示各种光分布。当使用不同的光分布时,虚拟地测试测试人员的接受度和安全感。此外,进一步的研究将测试司机的能见度因新产生的光分布而发生变化的程度。
英文摘要
The research project deals with the development of an AI-based control of the automotive light distribution to generate situationally optimized, dynamic light distributions. The aim of this research project is to break away from the conventional division of light distributions into low beam and high beam and to present holistic light distributions. In order to develop these light distributions, extensive studies will be carried out to record the current traffic area. Here, representative data for the German traffic area will be recorded first. The aim of the planned drives is to record the German traffic area in its entirety, taking into account different road classes analogous to their real occurrence. For the evaluation, the latest algorithms for the recognition of objects are trained individually and geometric distributions of different objects, cars, trucks, buses (each driving and parking), traffic signs (depending on class), pedestrians, cyclists are created. From these object distribution data as well as other recorded data such as speed, road class, road conditions etc. different traffic situations are automatically created.In addition to the recording of the German traffic area, contrast investigations and luminance analyses in the different road classes are carried out. These serve to determine the safety-relevant light requirements for motor vehicle drivers and to record the current light conditions in the German traffic area. Not only the recognizability distance and the necessary contrast are considered, but also the perception of brightness and homogeneity of the foreground illumination. This is essential, since in addition to an objective increase in safety, the subjective perception of safety also has a strong influence on the driver's well-being and behavior. The results of these investigations are also incorporated into the optimization of the situation-dependent light distributions.In addition, a further study records the gaze behavior of the drivers and calculates optimized light distributions by combining the object distributions and the gaze behavior in road traffic.These theoretical light distributions are then validated. In a first step, the various light distributions are displayed in a driving simulator, independent of the technology used, in the various virtually generated traffic situations. The acceptance and the feeling of safety of the test persons is tested virtually when using the different light distributions. In addition, a further study will test the extent to which drivers' visibility changes as a result of the newly generated light distributions.
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    52301178
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