Daimler Center for Automotive Information Technology Innovations

Today's Advanced Driver Assistance Systems (ADAS) adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. In order to further enhance their benefit, ADAS need to cooperate in the future. This enables, for instance, to solve hazardous situations by coordinated maneuvers for safety intervention on multiple vehicles at the same point in time. Our prototyping environment presented in previous work addresses developing such cooperative ADAS. Its underlying approach is to either bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development, or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. In this paper, we focus on handling the application specific parameter space, and more precisely on the scenario related aspects. As a part of our iterative prototyping process, defining and tuning scenarios and application parameters are highly repetitive tasks which needs to be designed very efficiently. We, therefore, strive to create a scenario definition methodology, which provides best flexibility and a minimal expenditure of time on the developer side.

The presented research work aims to enhance the overall simulation performance of V2X simulation systems. For this purpose, an advanced performance and scalability analysis regarding the influences of simulators' interactions performed during V2X simulations is introduced. It is to be shown, which influences parameters like the granularity of a simulator, the dimensions of the simulation scenario, the traffic density, or the number of simulated V2X-based vehicles on the performance have. This study shall help to achieve a deeper insight into aspects significantly influencing the simulation performance and, thus, to increase the simulation speed without any loss of accuracy and correctness.

In context-aware computing, the behavior of an application highly depends upon the environment. Recently, context-aware applications also became aware of tracing the user behavior and its environment-specific characteristics. These user-specific information are used to adapt an user interface in case the system recognizes a frequently visited environment. Unfortunately, it is still a challenging task to test a context-aware application that evolves over time. The lack of simulation data containing recurrent user behavior in the form of frequently visited environments as well as time and resource constraints for real time tests make it almost impossible to test the functionality or robustness. In this paper we describe a model-based approach to generate simulation data for user-centric and context-aware applications. The input of a context-aware application usually consists of sensor-related events. Hence, we are mainly focusing onto the generation of similar discrete event sequences that are supposed to simulate recurrent user behavior. The approach combines discrete event system specification with quantitative simulation in order to generate event sequences that are based on a scenario with qualitatively specified constraints.

During the past years, evolutionary testing research has reported encouraging results for automated functional (i.e. black-box) testing. However, despite promising results, these techniques have hardly been applied to complex, real-world systems and as such, little is known about their scalability, applicability, and acceptability in industry. In this paper, we describe the empirical setup used to study the use of evolutionary functional testing in industry through two case studies, drawn from serial production development environments at Daimler and Berner & Mattner Systemtechnik, respectively. Results of the case studies are presented, and research questions are assessed based on them. In summary, the results indicate that evolutionary functional testing in an industrial setting is both scalable and applicable. However, the creation of fitness functions is time-consuming. Although in some cases, this is compensated by the results, it is still a significant factor preventing functional evolutionary testing from more widespread use in industry.

Vehicular traffic congestion is a global phenomenon that has increased in importance in the last decades and has caused economically and ecologically negative effects. Thus, finding a way to improve traffic efficiency is a high-frequented problem to be solved by scientists and politicians worldwide. One new promising approach is the usage of decentralized wireless vehicle to vehicle communication based on the Vehicle-2-X (V2X) technology. The idea is that vehicles share information about the current local traffic situation and use this information to optimize their routes. In this paper, we introduce a new algorithm that can be used by navigation systems to calculate routes circumnavigating congested roads. For this purpose, each vehicle transmits its average speed of a road segment to vehicles in the neighbourhood. As a result, vehicles receiving this information can recalculate their routes based on the knowledge about the current possible speeds in the road segments of their neighbourhood. To evaluate the improvements that can be achieved by our algorithm, simulations have been done. Our results show that navigation systems using the V2X technology for a more intelligent route calculation can improve the traffic efficiency of future transport systems.