A Research and Educational Robotic Testbed for Real-time Control of Emerging Mobility Systems: From Theory to Scaled Experiments

TL;DR

This paper introduces IDS$^3$C, a scaled robotic testbed for real-time control of emerging mobility systems like CAVs, enabling safe, repeatable experiments and educational opportunities from simulation to real-world scenarios.

Contribution

The paper presents a novel 1:25 scaled testbed for testing and educational purposes, along with a real-time control framework for coordinating CAVs in various traffic scenarios.

Findings

Effective real-time control framework demonstrated in multiple traffic scenarios

IDS$^3$C enables safe, scalable testing of emerging mobility systems

Educational impact through hands-on training and interactive demos

Abstract

Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, and electric vehicles, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, before connectivity and automation are deployed en masse, a thorough evaluation of CAVs is required-ranging from numerical simulation to real-world public roads. Assessment of the performance of CAVs in scaled testbeds has recently gained momentum due to the flexibility they offer to conduct quick, repeatable experiments that could go one step beyond simulation. This article introduces the Information and Decision Science Lab's Scaled Smart City (IDS$^3$C), a 1:25 research and educational scaled robotic testbed that is capable of replicating different real-world urban traffic scenarios. IDS$^3$C was designed to investigate the effect of emerging mobility systems on safety and transportation efficiency. On the educational front, IDS$^3$C can be used for (a) training and educating graduate students by exposing them to a balanced mix of theory and practice, (b) integrating the research outcomes into existing courses, (c) involving undergraduate students in research, (d) creating interactive educational demos, and (e) reaching out to high-school students. In our exposition, we also present a real-time control framework that can be used to coordinate CAVs at traffic scenarios such as crossing signal-free intersections, merging at roadways and roundabouts, cruising in congested traffic, passing through speed reduction zones, and lane-merging or passing maneuvers. Finally, we provide a tutorial for applying our framework in coordinating robotic CAVs to a multi-lane roundabout scenario and a transportation corridor in IDS$^3$C.