Methodology to Estimate Load Spectra of Autonomous and Highly Automated Vehicles

The knowledge of representative load collectives and duty cycles is crucial for designing and dimensioning vehicles and their components. For human driven vehicles, various methods are known for deriving these load spectra directly or indirectly from fleet measurement data of the customer vehicle operation. Due to the lack of market penetration of highly automated and autonomous vehicles, there is no sufficient fleet data available to utilize these methods. As a result of increased demand for ride comfort compared to human driven vehicles, autonomous vehicle operation promises reduced driving speeds as well as reduced lateral and longitudinal accelerations. This can consequently lead to decreasing operation loads, thus enabling potentially more light-weight, cost-effective, resource-saving and energy-efficient vehicle components. In order to unlock this potential of dedicatedly dimensioned components for autonomous vehicles, a methodology for quantifying the loads in customer operation is required. Therefore, this paper proposes a novel methodology to quantify operation loads of highly automated and autonomous vehicles based on statistical long-term simulation, in which route characteristics, surrounding traffic and vehicle control algorithms are taken into account. The statistical synthesis of driving routes as the basis for further long-term simulation is addressed in detail in this paper. Furthermore, the impact of different lateral and longitudinal control strategies on drivetrain loads of an autonomous vehicle is showcased as an early result of the proposed methodology. Future work required to complete the proposed methodology is addressed in the outlook of this paper. Additional utilization of the driving route synthesis for the validation of autonomous driving functions is pointed out.