Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes

• Main Authors: Felix Batsch, Alireza Daneshkhah, Vasile Palade, Madeline Cheah
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Applied Sciences
• Subjects: gaussian process; probabilistic sensitivity analysis; machine learning; safe automated vehicles; scenario-based testing; logical scenario
• Online Access: https://www.mdpi.com/2076-3417/11/2/775

Assuring the safety of automated vehicles is essential for their timely introduction and acceptance by policymakers and the public. To assess their safe design and robust decision making in response to all possible scenarios, new methods that use a scenario-based testing approach are needed, as testing on public roads in normal traffic would require driving millions of kilometres. We make use of the scenario-based testing approach and propose a method to model simulated scenarios using Gaussian Process based models to predict untested scenario outcomes. This enables us to efficiently determine the performance boundary, where the safe and unsafe scenarios can be evidently distinguished from each other. We present an iterative method that optimises the parameter space of a logical scenario towards the most critical scenarios on this performance boundary. Additionally, we conduct a novel probabilistic sensitivity analysis by efficiently computing several variance-based sensitivity indices using the Gaussian Process models and evaluate the relative importance of the scenario input parameters on the scenario outcome. We critically evaluate and investigate the usefulness of the proposed Gaussian Process based approach as a very efficient surrogate model, which can model the logical scenarios effectively in the presence of uncertainty. The proposed approach is applied on an exemplary logical scenario and shows viability in finding concrete critical scenarios. The reported results, derived from the proposed approach, could pave the way to more efficient testing of automated vehicles and instruct further physical tests on the determined critical scenarios.