Prof. Dr.-Ing. habil.

Trapp, Mario

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  • Publication
    Fuzzy Interpretation of Operational Design Domains in Autonomous Driving
    ( 2022-07)
    Salvi, Aniket
    ;
    Weiß, Gereon
    ;
    Trapp, Mario
    ;
    Oboril, Fabian
    ;
    Buerkle, Cornelius
    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 μODDs. 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 μODDs, 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.
  • Publication
    Safety Implications of Runtime Adaptation to Changing Operating Conditions
    ( 2022)
    Salvi, Aniket
    ;
    Weiß, Gereon
    ;
    Trapp, Mario
    ;
    Oboril, Fabian
    ;
    Buerkle, Cornelius
    With further advancements of autonomous driving, also larger application scenarios will be addressed, so-called Operational Design Domains (ODDs). Autonomous vehicles will likely experience varying operating conditions in such broader ODDs. The implications of changing operating conditions on safety and required adaptation is, however, an open challenge. In our work, we exemplary investigate a vehicle following scenario passing through altering operating conditions and Responsibility Sensitive Safety (RSS) as formal model to define appropriate longitudinal following distances. We provide a deeper analysis of the influence of switching the safety model parameter values to adapt to new operating conditions. As our findings show that hard switches of operating conditions can lead to critical situations, we propose an approach for continuously adapting safety model parameters allowing for a safe and more comfortable transition. In our evaluation, we utilize driving simulations to compare the hard switching of parameters with our proposal of gradual adaptation. Our results highlight the implications of changing operating conditions on the driving safety. Moreover, we provide a solution to adapt the safety model parameters of an autonomous vehicle in such a way that safety model violations during transition can be avoided.