Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality

TL;DR

This paper explores how autonomous vehicles can safely and efficiently operate alongside human-driven vehicles by modeling human decision-making with bounded rationality and employing uncertainty-aware planning for AVs, analyzing their interactions and learning performance.

Contribution

It introduces a novel formulation of AV-HV interactions considering bounded rationality and uncertainty-aware planning, providing insights into their interplay and impact on learning performance.

Findings

Discovery of Goodhart's Law effect in AV learning performance

Identification of compounding effects in HV decision making

Analysis of how decision strategies influence overall learning outcomes

Abstract

The past few years have witnessed a rapid growth of the deployment of automated vehicles (AVs). Clearly, AVs and human-driven vehicles (HVs) will co-exist for many years, and AVs will have to operate around HVs, pedestrians, cyclists, and more, calling for fundamental breakthroughs in AI designed for mixed traffic to achieve mixed autonomy. Thus motivated, we study heterogeneous decision making by AVs and HVs in a mixed traffic environment, aiming to capture the interactions between human and machine decision-making and develop an AI foundation that enables vehicles to operate safely and efficiently. There are a number of challenges to achieve mixed autonomy, including 1) humans drivers make driving decisions with bounded rationality, and it remains open to develop accurate models for HVs' decision making; and 2) uncertainty-aware planning plays a critical role for AVs to take safety maneuvers in response to the human behavior. In this paper, we introduce a formulation of AV-HV interaction, where the HV makes decisions with bounded rationality and the AV employs uncertainty-aware planning based on the prediction on HV's future actions. We conduct a comprehensive analysis on AV and HV's learning regret to answer the questions: 1) {How does the learning performance depend on HV's bounded rationality and AV's planning}; 2) {How do different decision making strategies impact the overall learning performance}? Our findings reveal some intriguing phenomena, such as Goodhart's Law in AV's learning performance and compounding effects in HV's decision making process. By examining the dynamics of the regrets, we gain insights into the interplay between human and machine decision making.