# Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems
## 1 Introduction  
In recent years, the development and adoption of Automated Driving Functions (ADF) have substantially increased. All major OEMs have started to include various assisting features such as Lane-Keep-Assist (LKA), Adaptive-Cruise-Control (ACC), parking assist, traffic jam assist and others, into their fleets. All with the same goal, to provide the customer with increased safety and comfort. By definition, these systems represent level 1-2 automation with respect to the 6 levels of automation defined by the SAE [1]. However, test vehicles with higher level of automation, enabling fully unsupervised driving on defined street networks, are used for promotion and advertisement of research capabilities. In addition, test applications for automated transport, still with a supervisor on board, have been launched. Beside OEMs, various research groups are working on novel technologies which provide enhancements in safety and efficiency of passenger vehicles. On one side, we have advanced vehicle state estimation [2; 3; 4], followed by safe and agile maneuvering in extreme conditions [5], and improved control of steering feedback to the driver [6]. On the other side, improved safety is considered in several areas, starting from a shared control system where the human and ADFs are working in synergy [7], to research focusing on pedestrian safety [8], and in the development of future human-machine interfaces that should reduce driver distraction [9]. Optimal and efficient driving will play a huge role in the future of automation as more and more vehicles become aware of their dynamic surroundings [10]. However, testing and validation of these functions and their complex interactions are still among the biggest challenges in the field. Demonstrating the reliability, safety, and robustness of thetechnology in all conceivable situations, e.g., in all possible scenarios under all potential road and weather conditions, has been identified as the main roadblock for product homologation, certification and thus commercialization. It is also well known and accepted that time and cost-wise it is not feasible to drive the required 240 million kilometers [11] manually with every vehicle variant to achieve a proven-in-use certification. In addition, for every sensor and software update the same number of kilometers would have to be repeated. The focus of recent research in this area has been the development of methods and tools that reduce the number of driven kilometers for certification [12]. Currently, the main expectation is that only a clever combination of methods selecting the important test-cases and test-environments including Software in the loop (SiL), Hardware in the loop (HiL), Vehicle in the loop (ViL) and test-track and public road will lead to a feasible reduction in current testing efforts. It is clear that many kilometers will be driven purely virtually. The advantages of virtual tests are, beside others, the independence from real time and the possibility to execute the tests in a safe and repeatable environment. Nevertheless, the behavior of the ADFs needs to be continuously assessed and monitored throughout the whole tool chain. The data need to be classified by scenario type and severity level of safety and/or comfort issues. Currently, the classification is based on heuristic algorithms [13; 14], where engineering experts spend a lot of time and effort to create the necessary rules. For each detected driving scenario, various Key Performance Indicators (KPI) are calculated to reflect the driving sensation of the passengers. However, some signal patterns are very challenging to be detected by heuristic algorithms only. There are no distinct triggers where a threshold could be set. Instead, the signals usually show decreasing and increasing slopes containing noise that could lead to misclassification. Furthermore, due to sensor noise or false target detections some information gets lost and therefore some interesting driving scenario cannot be detected correctly by the heuristics.