# Revealed Multi-Objective Utility Aggregation in Human Driving

arXiv: https://arxiv.org/abs/2303.07435 · HTML: https://mlsanity.com/paper/2303.07435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans](https://mlsanity.com/paper/2001.04377/) (2020)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Preference learning along multiple criteria: A game-theoretic perspective](https://mlsanity.com/paper/2105.01850/) (2021)
- [Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles](https://mlsanity.com/paper/1904.10158/) (2019)
- [Solution Concepts in Hierarchical Games under Bounded Rationality with Applications to Autonomous Driving](https://mlsanity.com/paper/2009.10033/) (2020)
- [A Formal Separation Between Strategic and Nonstrategic Behavior](https://mlsanity.com/paper/1812.11571/) (2018)
- [Generalized dynamic cognitive hierarchy models for strategic driving behavior](https://mlsanity.com/paper/2109.09861/) (2021)

## Abstract (arXiv)

A central design problem in game theoretic analysis is the estimation of the players' utilities. In many real-world interactive situations of human decision making, including human driving, the utilities are multi-objective in nature; therefore, estimating the parameters of aggregation, i.e., mapping of multi-objective utilities to a scalar value, becomes an essential part of game construction. However, estimating this parameter from observational data introduces several challenges due to a host of unobservable factors, including the underlying modality of aggregation and the possibly boundedly rational behaviour model that generated the observation. Based on the concept of rationalisability, we develop algorithms for estimating multi-objective aggregation parameters for two common aggregation methods, weighted and satisficing aggregation, and for both strategic and non-strategic reasoning models. Based on three different datasets, we provide insights into how human drivers aggregate the utilities of safety and progress, as well as the situational dependence of the aggregation process. Additionally, we show that irrespective of the specific solution concept used for solving the games, a data-driven estimation of utility aggregation significantly improves the predictive accuracy of behaviour models with respect to observed human behaviour.