Cooperative Advisory Residual Policies for Congestion Mitigation
Author(s)
Hasan, Aamir
Chakraborty, Neeloy
Chen, Haonan
Cho, Jung-Hoon
Wu, Cathy
Driggs-Campbell, Katherine
Date Issued
October 8, 2024
Journal
ACM Journal on Autonomous Transportation Systems
Publisher
ACM
Citation
Hasan, Aamir, Chakraborty, Neeloy, Chen, Haonan, Cho, Jung-Hoon, Wu, Cathy et al. 2024. "Cooperative Advisory Residual Policies for Congestion Mitigation." ACM Journal on Autonomous Transportation Systems.
Version
Final published version
Abstract
Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers? reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3699519
