Complexity in cooperative traffic situations: Discrete-event modeling in automated vehicle guidance

研究问题。该项目涉及的主题情况预测和系统人机工程学。在许多交通情况下，尽管有有效的交通规则，但当合作行为缺失时，将观察到故障。这种情况经常发生在城市X形和T形交叉口或同等狭窄的通道中。此外，我们以前的工作表明，合作的需要也可能会自发地出现时，交通情况的解决方式与它应该是不同的。此外，假设交通状况的复杂性对人的合作行为具有重要影响。我们的目标是更好地了解人类在这种情况下的行为：哪些环境信息与评估合作环境有关？如何识别合作伙伴的行为意图？在这些发现的基础上，将推导出一种用于情况预测的算法，以便自动车辆也能够与人类驾驶员合作克服死锁情况。研究方法和结果。为了实现这些目标，需要进行实证研究：除了在“Testfeld Autonomes Fahren巴登-符滕贝格”进行实地实验研究和观察交通研究外，还计划在驾驶模拟器中进行进一步的实验。由于我们假设人类的合作行为高度依赖于交通的复杂性，应提出一个合适的措施，允许自动评估。在此基础上，时间依赖的随机模型的事件离散描述的合作情况下，应开发，以预测合作伙伴的可能的意图，并准备一个最佳的行为决策，在汽车内的乘客和其他道路使用者接受。最后，该算法应在实时框架内实现，并将在测试车辆中进行演示。作为观测交通研究的一项成果，预计将建立一个庞大的数据库，供其他研究小组使用。

Research question. The project addresses the topics situation prediction and system ergonomics. In many traffic situations a breakdown will be observed when cooperation behavior is missing, despite of valid traffic rules. Such deadlock situations occur in urban X- and T-intersections or in coequal narrow passages regularly. Besides, our previous work has shown that a need of cooperation may also arise spontaneously when a traffic situation is solved differently than it is supposed to be. Furthermore, it is to be assumed that the complexity of a traffic situation has an important impact on the human cooperation behavior. Our objective is to gain a better understanding of how humans behave in such situations: Which environmental information is relevant for assessing a cooperative setting? How do we recognize the behavioral intention of cooperation partners? On the basis of these findings, an algorithm for situation prediction shall be derived, so that automatic vehicles are also able to overcome deadlock situations in cooperation with human drivers. Research methods and results. In order to achieve these goals, empirical studies are to be carried out: Besides a field experimental study and an observational traffic study within „Testfeld Autonomes Fahren Baden-Württemberg“ further experiments in a driving simulator are planned. As we assume that human cooperation behavior depends highly on traffic complexity, a suitable measure shall be proposed that allows for an automatic assessment. On this basis time-dependent stochastic models for the event-discrete description of cooperation situations shall be developed, in order to predict the likely intentions of cooperation partners and to prepare for an optimal behavioral decision, accepted by both the passengers in the car and other road users. Finally, the algorithm shall be implemented within a real-time framework and it will also be demonstrated in a testing vehicle. As an outcome of the observational traffic study a huge database is to be expected, which shall be made available to other research groups.