Instinctive Negotiation by Autonomous Agents in Dense, Unstructured Traffic: A Controls Perspective

Abstract

Operating autonomous agents in unstructured space presents a difficult problem. The complexity of making decisions such as when to yield and when to go ahead increases exponentially with the number of agents. This is true for humans as well as for software that controls autonomous agents. With some practice, however, human operators are able to move efficiently in a maze of interacting agents in dense traffic. One recent result correlates the instability of equilibria in a multiagent system with an absence of gridlocks. These control barrier function–based algorithms do not include a decision-making component—the action is continuous, and negotiation happens through instability. This mechanism, referred to as instinctive negotiation, is contrasted with discontinuity-induced decisions arising from nonconvex optimization. Based on observed behavioral similarities and insights into human implicit and explicit learning, this article proposes a connection with human driving and suggests that humans may employ a mechanism similar to instinctive negotiation to navigate dense traffic. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.