Scenario-Based Risk Quantification Approach for Assuring Safety in Autonomous Vehicles

Contemporary cutting-edge technologies, such as automated driving brought up vital questions about safety and relativized the safety assurance and acceptance criterion on different aspects. New risk assessment, evaluation, and acceptance justifications are required to assure that the assumptions and benchmarking are made on a reasonable basis. While there are some existing risk evaluation methods, most of them are qualitative in nature and are subjective. Moreover, information such as the safety performance indicators (SPIs) of the sensors, algorithms, and actuators are often not utilized well in these methods. To overcome these limitations, in this paper we propose a risk quantification methodology that uses Bayesian Networks to assess if the residual risk is reasonable under a given scenario. Our scenario-based methodology utilizes the SPIs and uncertainty estimates of sensors, algorithms, and actuators as well as their characteristics to quantify risk using the conditional probability tables that assure no dependencies among vehicle’s elements are overlooked. We also discuss the guidelines that need to be followed when creating the probability tables. To illustrate our methodology, we use a running example, in which we demonstrate how we calculate the risk using our Bayesian approach. We also discuss the merits and limitations of our proposed methodology, and how it is helpful even when we might not have sufficient information from suppliers.