# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
## 4 Discussion  
The intention of the presented work is to contribute to the development of future AV that are not only safe, but can also move cooperatively in road traffic. Therefore, we presented a method for extracting test scenarios from real-world traffic data that are challenging from the perspective of cooperative behavior. By applying this method to a highway traffic dataset, we created a catalog of test scenarios that allows comparison and benchmarking of different cooperative maneuver planning algorithms. Since the used highD dataset [29] is only accessible after permission, the scenario catalog cannot be shared directly. Instead, we provide a Python tool that extracts the scenarios of the test catalog by their recording- and vehicle IDs, provided there is access to the root dataset. In contrast to the state-of-the-art of test scenario generation for AV, which so far addressed the topics of safety evaluation and driving task completion of single vehicles, the presented work aims to test an AV's ability of cooperatively interacting with other road vehicles. This requires test scenarios that demand cooperative behavior of at least one of the involved vehicles. The presented scenario extraction approach can select scenarios with this characteristic, which represents the main contribution to the state-of-the-art of AV test scenario generation. Furthermore, the presented results extend the state-of-the-art for test scenarios for cooperative maneuver planning, which has so far consisted of artificially generated scenarios, by a data driven approach. It was shown that, due to the presented selection process of scenarios, a condensed  
Figure 7: Relative frequency of cooperative actions. representation of cooperative situations in road traffic is possible. Since the data set created with this method contains a large number of scenarios that are difficult to think up, but which occur in real traffic, it can also be used in the development process of new behavior planners. The extracted scenarios are clustered according to the most cooperative action that is performed by one of the vehicles in the scenario. Analyzing these scenario clusters shows that the exact sequence of the scenarios varies, but that the cooperative mechanisms are the same within each cluster. Therefore, the performance evaluation of a tested cooperative behavior planner allows conclusions to be drawn about the situations in which the planner works well and in which there is room for improvement. One limitation of the test scenario catalog is that the amount of data in the root dataset is limited, which results in a small number of two-lane and especially three-lane merge scenarios. Therefore, the result of these roadway types could be less representative compared to the three-lane scenarios. This can only be overcome by a larger amount of data. Since the presented method is independent from the root dataset, it could be applied to a larger dataset released in the future. In case of a much larger dataset, it might not be applicable to use all extracted scenarios for testing. Therefore, the selection of representative test scenarios as well as the exclusion of similar ones will be a topic of future work. Another point of discussion is whether a foresightful planning automated vehicle ended up in difficult situations like the ones in the dataset or if it would be able to avoid them beforehand. In the second case, the test scenarios, which are derived from human-operated traffic, would not be suitable for AV. Since a traffic situation arises as a result of the actions of all involved vehicles, and the surrounding vehicles of an automated vehicle will be mainly driven by humans in the near future, a behavior planner for AV must also be able to manage today's difficult traffic situations. The solution of some scenarios of the global planner show non-humanlike behavior, such as creating gaps behind a vehicle, e.g., when vehicle 1 performs in the exemplary scenario in Section 3.