Modeling and Analysis of an ADAS Overtaking System in Complex Driving Scenarios

This master thesis presents research targeting a driver assistance system where it is possible to understand the environment and prepare a safe path for overtaking when necessary. Overtaking is known as one of the most dangerous driving phases; therefore, creating systems aimed at reducing errors made by drivers and unambiguity in their actions is essential. This work is positioned within the rapidly evolving domain of vehicle automation that from the end of the year 2023 has allowed a SAE level higher than level 2. Furthermore, advanced simulation models are used to replicate realistic and challenging driving scenarios, testing the response of the system in critical situations such as overtaking with oncoming vehicles. These provide valuable insight into the performance of the system under critical circumstances and serve to further develop and test the capabilities of the system on different road conditions. For this type of system, a set of basic principles is thoroughly analyzed, including modeling each element from the theoretical standpoint and the possible use of adequate software. To accomplish this aim, a method for the construction and validation of the performing system, irrespective of the specific scenario of its operation, is developed. Several test cases are constructed with the aim of identifying possible worst-case scenarios of the system. This makes it possible to analyze both satisfactory and unsatisfactory outcomes.