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: --
• 依托单位: Fachgebiet für Adaptive Lichttechnische Systeme und Visuelle Verarbeitung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/450942921?language=en
• 关键词: AI; based; light; distribution; optimization

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.

该研究项目涉及开发基于AI的汽车光分布控制，以生成情境优化的动态光分布。本研究的目的是打破传统的光分布分为近光灯和远光灯，并提出整体的光分布。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。在此，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，同时考虑到与其真实的发生类似的不同道路等级。对于评估，最新的算法识别的对象单独训练和几何分布的不同对象，汽车，卡车，公共汽车（每个驾驶和停车），交通标志（取决于类），行人，骑自行车的人被创建。根据这些目标分布数据以及其他记录数据，如速度、道路等级、道路状况等，自动创建不同的交通状况。除了德国交通区域的记录外，还进行不同道路等级的对比调查和亮度分析。这些用于确定机动车驾驶员的安全相关照明要求，并记录德国交通区域的当前照明条件。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。这是必不可少的，因为除了客观上的安全性增加外，安全的主观感知也对驾驶员的健康和行为有很大的影响。此外，进一步研究了驾驶员的注视行为，并结合道路交通中的目标分布和注视行为计算了优化的光分布，验证了理论光分布的有效性。在第一步中，在驾驶模拟器中显示各种光分布，与所使用的技术无关，在各种虚拟生成的交通情况下。当使用不同的光分布时，测试人员的接受度和安全感被虚拟地测试。此外，进一步的研究将测试驾驶员的能见度变化的程度，作为新产生的光分布的结果。