  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.
