A merging interaction model explains human drivers' behaviour from input signals to decisions
CoRR(2023)
Abstract
One of the bottlenecks of automated driving technologies is safe and socially
acceptable interactions with human-driven vehicles, for example during merging.
Driver models that provide accurate predictions of joint and individual driver
behaviour of high-level decisions, safety margins, and low-level control inputs
are required to improve the interactive capabilities of automated driving.
Existing driver models typically focus on one of these aspects. Unified models
capturing all aspects are missing which hinders understanding of the principles
that govern human traffic interactions. This in turn limits the ability of
automated vehicles to resolve merging interactions. Here, we present a
communication-enabled interaction model based on risk perception with the
potential to capture merging interactions on all three levels. Our model
accurately describes human behaviour in a simplified merging scenario,
addressing both individual actions (such as velocity adjustments) and joint
actions (such as the order of merging). Contrary to other interaction models,
our model does not assume humans are rational and explicitly accounts for
communication between drivers. Our results demonstrate that communication and
risk-based decision-making explain observed human interactions on multiple
levels. This explanation improves our understanding of the underlying
mechanisms of human traffic interactions and poses a step towards
interaction-aware automated driving.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined