Time-to-approach-based model predictive controller for autonomous vehicle safety during unexpected wheelchairs on the path

Abstract

ABSTRACTThe objectives of this study are to (i) develop a Time-to-Approach (TTA)-based Model Predictive Controller (MPC) for an Autonomous Vehicle (AV) to safely navigate around an unexpected wheelchair on its path and (ii) develop a prediction model to forecast a wheelchair's paths and potential collisions between it and an AV. Our analyses reveal that the TTA-based MPC enhances safety by reducing collision potential between an AV and a wheelchair by 44% when compared to the traditional distance-based MPC, as measured by the lateral deviation of an AV from the potential collision location. Furthermore, our approach improves passenger comfort by an average of 62% compared to the distance-based MPC.KEYWORDS: People with disabilityAutonomous vehiclesafetynavigationmodel predictive controller AcknowledgmentsThis study is partially supported by the Center for Connected Multimodal Mobility (C2M2) (USDOT Tier 1 University Transportation Center) Grant headquartered at Clemson University, Clemson, South Carolina, USA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Center for Connected Multimodal Mobility (C2M2), and the U.S. Government assumes no liability for the contents or use thereof.Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsThe authors confirm contribution to the paper as follows: study conception and design: M. Islam, M. Chowdhury, M. Rahman, and Z. Khan.; data collection: M. Islam and Z. Khan.; interpretation of results: M. Islam, M. Chowdhury, M. Rahman, and Z. Khan; draft manuscript preparation: M. Islam, M. Chowdhury, M. Rahman, and Z. Khan. All authors reviewed the results and approved the final version of the manuscript.