Methods and models for safety benefit assessment of advanced driver assistance systems in car-to-cyclist conflicts

To help drivers avoid or mitigate the severity of crashes, advanced driver assistance systems (ADAS) can be designed to provide warnings or interventions. Prospective safety assessment of ADAS is important to quantify and optimise their safety benefit. Such safety assessment methods include, for example, virtual simulations and test-track testing.

Today, there are many components of virtual safety assessment simulations with models or methods that are missing or can be substantially improved. This is particularly true for simulations assessing ADASs that address crashes involving cyclists—a crash type that is not decreasing at the same rate as the overall number of road crashes in Europe. The specific methodological gaps that this work addresses are: a) computational driver models for car-to-cyclist overtaking, b) algorithms for model fitting and efficient calculation of ADAS intervention time, and c) a method for merging data from different data sources into the safety assessment.

Specifically, for a), different driver models for everyday driver behaviour while overtaking cyclists in a naturalistic driving setting were derived and compared. For b), computationally efficient algorithms to fit driver models to data and compute ADAS intervention time were developed for different types of vehicle models. The algorithms can be included in ADAS both for offline use in virtual assessment simulations and online real-time use in in-vehicle ADAS. Lastly, for c), a method was developed that uses Bayesian statistics to combine results from different data sources, e.g., simulations and test-track data, for ADAS safety benefit assessment.

In addition to presenting five peer-reviewed scientific publications, which address these issues, this compilation thesis discusses the use of different data sources; introduces the fundamentals of Bayesian inference, linear programming, and numerical root-finding algorithms; and provides the rationale for methodological choices made, where relevant. Finally, this thesis describes the relationships among the publications and places them into context with existing literature.

This work developed driver models for the virtual simulations and methods for the reliable estimation of the prospective safety benefit, which together have the potential to improve the design and the evaluation of ADAS in general, and ADAS for the car-to-cyclist overtaking scenario in particular.

Jordanka Kovaceva
Research area
Systems for accident prevention and AD
Publication type
Doctoral thesis
Published in
Year of publication