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Flexible Stochastic Microscopic Traffic Model for ADAS Testing

机译：用于ADAS测试的灵活随机微观交通模型

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Methods to assess the safety of advanced driving assistance systems (ADAS) and further highly automated vehicles (HAV) are of paramount importance. There is a wide consensus that virtual testing will be part of them, as physical testing will not be even nearly exhaustive. Virtual testing requires models of the controlled vehicle, but also of the surrounding traffic, which for a long time will consist mainly of human-driven vehicles. Many driver and traffic models exist, but they are usually tailored for a specific situation or requirement. As the reaction of human drivers is affected by many different factors, like the traffic conditions, the time or the country, there is a need for a flexible structure which can be easily tuned to different situations. If we split the driver reaction in a decision and an actuation step, we argue that the actuation step can be represented by few stochastic actuation models which do not depend strongly on external factors. This paper shows such models and their performance with highway data both from China and Germany.
机译：评估高级驾驶辅助系统（ADAS）和进一步的高度自动化车辆（HAV）的安全性的方法至关重要。人们普遍认为虚拟测试将成为其中的一部分，因为物理测试甚至不会穷尽。虚拟测试不仅需要受控车辆的模型，还需要周围交通的模型，长期以来，模型将主要由人为驾驶的车辆组成。存在许多驾驶员和交通模型，但是它们通常是针对特定情况或要求量身定制的。由于人类驾驶员的反应受许多不同因素的影响，例如交通状况，时间或国家，因此需要一种易于调整以适应不同情况的灵活结构。如果我们将驾驶员的反应分为决策和致动步骤，则我们认为致动步骤可以由很少依赖于外部因素的随机致动模型表示。本文通过中国和德国的公路数据显示了此类模型及其性能。

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