If you are familiar with the real-life series then many of the tracks and cars will be familiar to you.Ĭurrently, ACC host a range of the SRO series including the GT World Challenge Europe (formerly Blancpain Sprint & Endurance series) 2018, 2019 2020 season, the 2019 Intercontinental GT Challenge and a number of tracks from the UK and American GT series. The majority of the content within ACC will focus on the SRO licenced series. Without some of these packs, there are pieces of content you will not be able to experience within the sim. The main point of interest before purchasing is that ACC comes with several different Downloadable Content (DLC) packs that add a host of new cars and tracks to the game. Before You Beginīefore you go to steam and buy the first edition you see of ACC there are a few details that need to be understood. Additionally, throughout the guide there will be a number of other articles linked within that will go into more detail on particular topics. The guide has been broken down into six different steps so if you already have a head start skip to the section you need help with. So much so that we have decided to put together the beginner’s guide to Assetto Corsa Competizione. Here at The Coach Dave Academy, we want to ensure that anyone who wants to start sim racing can jump in and have fun no matter their experience or skill level. The ability to identify and imitate individual driving styles does not only support the performance optimisation of race cars but could also aid the development of road cars and driver assistance systems in future work.Getting started in the world of sim racing can be a daunting task with a whole host of jargon, unspoken rules and just general complexity to the setup of your chosen sim. An evaluation with data from professional race drivers shows the capability of DIMRA to derive metrics which describe human race driving styles, as well as ProMoD to robustly generate competitive laps with human-like controls in a professional motorsport driving simulator. Supported by this knowledge, we extend and adapt the imitation learning framework Probabilistic Modeling of Driver Behavior (ProMoD) in order to model race drivers in a complex simulation environment. We develop the Driver Identification and Metric Ranking Algorithm (DIMRA) as a data-based method for an in-depth objective analysis and assessment of professional race drivers. In this paper, we present a holistic approach to identify and model individual race driving styles in a robust way. At the same time, an objective assessment and especially imitation of professional race drivers is difficult due to individual driving styles, complex and non-deterministic decision making processes, and small stability margins. A good understanding and modelling of the human driver is essential for modern vehicle development, particularly in motorsports, where the race car should fit its driver perfectly.
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