# Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control

arXiv: https://arxiv.org/abs/2503.17993 · HTML: https://mlsanity.com/paper/2503.17993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [On the Forces of Driver Distraction: Explainable Predictions for the Visual Demand of In-Vehicle Touchscreen Interactions](https://mlsanity.com/paper/2301.02065/) (2023)
- [How Do Drivers Self-Regulate their Secondary Task Engagements? The Effect of Driving Automation on Touchscreen Interactions and Glance Behavior](https://mlsanity.com/paper/2207.04284/) (2022)
- [Modeling human road crossing decisions as reward maximization with visual perception limitations](https://mlsanity.com/paper/2301.11737/) (2023)
- [Multitasking while Driving: How Drivers Self-Regulate their Interaction with In-Vehicle Touchscreens in Automated Driving](https://mlsanity.com/paper/2305.16042/) (2023)

## Built on by (most cited first, 1 of 1)

- [Log2Motion: Biomechanical Motion Synthesis from Touch Logs](https://mlsanity.com/paper/2601.21043/) (2026)

## Abstract (arXiv)

Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and automation levels. Unlike previous models, it accounts for context-dependent multitasking across different degrees of driving automation. The model predicts longer in-car glances on straight roads and shorter glances during curves. It also anticipates increased glance durations with driver aids such as lane-centering assistance and their interaction with environmental demands. Validated against two empirical datasets, the model offers insights into driver multitasking amid evolving in-car technologies and automation.