# Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility

arXiv: https://arxiv.org/abs/2505.14983 · HTML: https://mlsanity.com/paper/2505.14983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 7 of 7)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness](https://mlsanity.com/paper/1811.06047/) (2018)
- [Multi-Task Trust Transfer for Human-Robot Interaction](https://mlsanity.com/paper/1807.01866/) (2018)
- [Toward Adaptive Trust Calibration for Level 2 Driving Automation](https://mlsanity.com/paper/2009.11890/) (2020)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)
- [A Workload Adaptive Haptic Shared Control Scheme for Semi-Autonomous Driving](https://mlsanity.com/paper/2004.00167/) (2020)
- [Wellbeing in Future Mobility: Toward AV Policy Design to Increase Wellbeing through Interactions](https://mlsanity.com/paper/2310.01669/) (2023)

## Abstract (arXiv)

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the form of a Dynamic Bayesian Network (DBN) that infers the cognitive states of both AV users and other road users, integrating this information into the AV's decision-making process. Specifically, our model captures the well-being of both an AV user and an interacting road user as cognitive states alongside trust. Our DBN models infer beliefs over the AV user's evolving well-being, trust, and intention states, as well as the possible well-being of other road users, based on observed interaction experiences. Using data collected from an interaction study, we refine the model parameters and empirically assess its performance. Finally, we extend our model into a causal inference model (CIM) framework for AV decision-making, enabling the AV to enhance user well-being and trust while balancing these factors with its own operational costs and the well-being of interacting road users. Our evaluation demonstrates the model's effectiveness in accurately predicting user's states and guiding informed, human-centered AV decisions.