Uncertainty Evaluation for Autonomous Vehicles: A Case Study of AEB System

To improve the safety of the intended functionality (SOTIF) performance of autonomous driving systems, this paper proposed a design method for autonomous driving systems with uncertainties. The automatic emergency braking (AEB) system is taken as an example to demonstrate the methodology. Firstly, uncertainty parameters in the AEB system model of typical working scenarios are defined and quantified, and a stochastic model of the AEB system with uncertainty parameters is established. Subsequently, the Monte Carlo simulation is employed to ascertain the actual safety distance distribution characteristics of the AEB system with uncertainties. The variance and width of the actual safety distance distribution are taken as response values to measure the reliability and robustness of the AEB system. The Box–Behnken design method is employed to design the uncertainty combination simulation test schemes. The surrogate models of uncertainty parameters with response variance and distributed width are established respectively, and the significance analyses are conducted. Finally, based on the variance surrogate models, the impact of uncertainties on the AEB system reliability and robustness is analyzed. This analysis provides the basis for the design of AEB system sensors. Based on the distributed width surrogate model, a dynamic safety distance adjustment mechanism is established to adjust the theoretical safety distance according to different uncertainties, thereby improving the reliability and robustness of the AEB system with multiple uncertainties. The method proposed in this paper provides a new idea for solving the SOTIF problems for autonomous driving systems.