Planning for Automated Vehicles with Human Trust
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Published:2022-10-31
Issue:4
Volume:6
Page:1-21
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ISSN:2378-962X
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Container-title:ACM Transactions on Cyber-Physical Systems
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language:en
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Short-container-title:ACM Trans. Cyber-Phys. Syst.
Author:
Sheng Shili1ORCID,
Pakdamanian Erfan1ORCID,
Han Kyungtae2ORCID,
Wang Ziran2ORCID,
Lenneman John3ORCID,
Parker David4ORCID,
Feng Lu1ORCID
Affiliation:
1. School of Engineering, University of Virginia
2. InfoTech Labs, Toyota Motor North America
3. Collaborative Safety Research Center, Toyota Motor North America
4. School of Computer Science, University of Birmingham
Abstract
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.
Funder
National Science Foundation
European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
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