Fuzzy Interpretation of Operational Design Domains in Autonomous Driving

The evolution towards autonomous driving involves operating safely in open-world environments. For this, autonomous vehicles and their Autonomous Driving System (ADS) are designed and tested for specific, so-called Operational Design Domains (ODDs). When moving from prototypes to real-world mobility solutions, autonomous vehicles, however, will face changing scenarios and operational conditions that they must handle safely. Within this work, we propose a fuzzy-based approach to consider changing operational conditions of autonomous driving based on smaller ODD fragments, called mu ODDs. By this, an ADS is enabled to smoothly adapt its driving behavior for meeting safety during shifting operational conditions. We evaluate our solution in simulated vehicle following scenarios passing through different mu ODDs, modeled by weather changes. The results show that our approach is capable of considering operational domain changes without endangering safety and allowing improved utility optimization.