# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
###### Abstract  
Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works in the presented scenario and thus no general statement can be made about the performance of the algorithm. Furthermore, there is a risk that fictitious traffic scenarios may be solved which do not occur in reality. Therefore, we present a procedure for generating test scenarios based on real-world traffic datasets that require cooperation of at least one of the involved vehicles and thus are challenging from the perspective of cooperation. This procedure is applied to a large highway traffic dataset, resulting in a test scenario catalog that allows a comprehensive performance evaluation. The extracted scenarios are clustered according to the cooperative actions used to solve the respective scenario, which enables a more detailed understanding of the underlying cooperative mechanisms. In order to serve as a basis for making comparisons between different behavior planners and thus contribute to the development of future maneuver planning algorithms, a tool to extract the test scenarios from the used traffic dataset is made publicly available.  
c 202015 November 202018 November 20202018 November 20202015 November 20202018 November 20202019 October 20202015 November 2020202015 November 2020202015 November 2020202015 November 2020202020202020202020202020202  
completion of driving tasks [9; 10; 11]. However, these only consider the performance of the AV under test and not the effects on other road users in terms of cooperative behavior. With the CommonRoad framework, [PERSON] et al. [12] introduced a platform for composing and sharing motion planning problems that consist of a scenario, a vehicle model, vehicle parameters, and cost functions. For each scenario, a ranking is provided, where users can upload and compare the results of their maneuver planners. Since CommonRoad only hosts the uploaded scenarios, it is dependent on the content generated by its community.  
Generating scenarios for testing AV cooperative behavior has been a less addressed topic. Initial works in this field propose artificially creating test scenarios. Whereas [PERSON] et al. [13] use a Simulation of Urban Mobility (SUMO) to detect interesting situations from the perspective of cooperation, [PERSON] et al. [14] create specific scenarios from a predefined logical scenario by randomly choosing the respective parameters from limited parameter spaces. After simulating the generated scenarios, a combined metric uses a threshold to identify critical situations.  
The main aim of this paper is to present a test scenario catalog for cooperative maneuver planning that is based on real traffic data. The advantage of the data-driven concept lies in the automated and realistic generation of scenarios. No manual effort is required to create the scenarios, and they are not constrained by the behavior model of traffic simulation programs or the imagination of the user generating the scenarios either. A further contribution to the state of the art is achieved by introducing a scenario selection approach that is able to decide if a certain scenario requires cooperation, which would thereby ensure that only relevant scenarios are included in the test scenario catalog. The presented scenario generation pipeline is applied to a state-of-the-art highway dataset and a tool to extract the computed scenarios is made publicly available on www.github.com/TUMFTM/test_scenarios_cooperation.