3 research outputs found
PeRP: Personalized Residual Policies For Congestion Mitigation Through Co-operative Advisory Systems
Intelligent driving systems can be used to mitigate congestion through simple
actions, thus improving many socioeconomic factors such as commute time and gas
costs. However, these systems assume precise control over autonomous vehicle
fleets, and are hence limited in practice as they fail to account for
uncertainty in human behavior. Piecewise Constant (PC) Policies address these
issues by structurally modeling the likeness of human driving to reduce traffic
congestion in dense scenarios to provide action advice to be followed by human
drivers. However, PC policies assume that all drivers behave similarly. To this
end, we develop a co-operative advisory system based on PC policies with a
novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises
drivers to behave in ways that mitigate traffic congestion. We first infer the
driver's intrinsic traits on how they follow instructions in an unsupervised
manner with a variational autoencoder. Then, a policy conditioned on the
inferred trait adapts the action of the PC policy to provide the driver with a
personalized recommendation. Our system is trained in simulation with novel
driver modeling of instruction adherence. We show that our approach
successfully mitigates congestion while adapting to different driver behaviors,
with 4 to 22% improvement in average speed over baselines.Comment: Accepted to ITSC 2023. Additional material and code is available at
the project webpage: https://sites.google.com/illinois.edu/per
Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph
We study the problem of safe and intention-aware robot navigation in dense
and interactive crowds. Most previous reinforcement learning (RL) based methods
fail to consider different types of interactions among all agents or ignore the
intentions of people, which results in performance degradation. In this paper,
we propose a novel recurrent graph neural network with attention mechanisms to
capture heterogeneous interactions among agents through space and time. To
encourage longsighted robot behaviors, we infer the intentions of dynamic
agents by predicting their future trajectories for several timesteps. The
predictions are incorporated into a model-free RL framework to prevent the
robot from intruding into the intended paths of other agents. We demonstrate
that our method enables the robot to achieve good navigation performance and
non-invasiveness in challenging crowd navigation scenarios. We successfully
transfer the policy learned in simulation to a real-world TurtleBot 2i