Year: 2017
Type: preprint
Abstract: Automated vehicles have been under heavy developments in major auto and tech companies and are expected to release into market in the foreseeable future. However, the road safety of these vehicles remains a concern. One approach to evaluate their safety is via on-track experimentation, but this requires gigantic costs and time investments. This paper discusses a sequential learning approach based on kriging models to reduce the experimental runs and economize on-track experimentation. The approach relies on a heuristic simulation-based gradient descent procedure to search for the best next test scenario. We demonstrate our approach with some numerical test cases.
Source: arXiv (Cornell University)
Authors: Huang, Zhiyuan, Lam, Henry, Zhao, Ding
Institution: University of Michigan
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: Collaborative Research: Modeling and Analyzing Extreme Risks in Insurance and Finance, G3387330441, A Sensitivity Approach to Assessing Model Uncertainty for Stochastic Systems, CAREER: Optimization-based Quantification of Statistical Uncertainty in Stochastic and Simulation Analysis, G6671297155
OpenAlex ID: W4297592491