# 2539 / Model Predictive Control of a Robot Driven Vehicle for Testing of Advanced Driver... Title: Model Predictive Control of a Robot Driven Vehicle for Testing of Advanced Driver Assist Systems Authors: Mike Huang, Haixuan Qiu, Chunyu Yang, Lian Xia, Zhaomin Lin, Yanqing Wang, Zongqing Xu, and Mingfu Tang Abstract: Advanced Driver Assist Systems (ADAS) are becoming more prevalent and more sophisticated in passenger vehicles, with features such as automatic lane keeping, pedestrian detection, and emergency breaking. In line with the increased production deployment of ADAS, testing of these systems are becoming more rigorous with more scenarios needing to be considered every year, see, for example, the ADAS testing conducted by Euro NCAP. To fit the need of placing test vehicles and environment factors in very specific and repeatable scenarios, physical driving robots are commonly used. While most current tests set up the test vehicle in near steady state conditions, e.g., constant speed and straight, in the future, more complex, possibly dynamic scenarios will need to be tested. This paper presents a Model Predictive Control (MPC) strategy for controlling a vehicle along dynamic paths and is easily deployable across different vehicles. Experiment results demonstrating the controller capability for both an electric and a conventional vehicle is presented. Order the full paper: ISTVS members: receive three papers per year as part of your membership via the ISTVS Member Portal: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://2022.istvs.org/papers/2539.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.