Integrating Smart Cars in Smart Cities: A Particle Swarm Problem

The challenge raised by the Department of Transportation to integrate autonomous vehicles (AV) within Smart Cities (where adequate infrastructure could support the safe and reliable functioning of AV technology), has led many researchers to look into novel and innovative ways to model the interaction of multiple vehicles. The study here proposed explores an unconventional approach to solve the problem of safely integrating smart vehicles within Smart Cities.  Specifically, this work will explore the creation of ad-hoc rules for coordinating the motion of multiple AVs that mimic swarm and flock movement (or particle swarm motion). The goal is that of generalizing what systems like adaptive cruise control do currently. Adaptive cruise control detects the relative position and speed of a lead vehicle and adjusts the vehicle’s speed to match or to avoid a collision. In the context of a smart city, we may not want to define a single lead vehicle, and this type of control can be generalized to a multi-body perspective by exploring the rules that govern swarms and flock movement in nature. Rules for such generalization will be derived in this work. Additionally, particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This work also explores the integration of the notion of hazard and danger levels (i.e., measures of the "closeness" to a given accident scenario, typically used in robotics) with the concept of safety distance for adaptive cruise control. Finally, the effect of short range V2V (vehicle to vehicle) and V2I (vehicle to infrastructure) communication on the proposed solutions will be explored.

Principal Investigator: 

Francesca M. Favaro

PI Contact Information:
San José State University


May 2017 to August 2018

Project Number: