Augmented Driver Behavior Models for High-Fidelity Simulation Study of
Crash Detection Algorithms
- URL: http://arxiv.org/abs/2208.05540v2
- Date: Wed, 26 Apr 2023 14:23:14 GMT
- Title: Augmented Driver Behavior Models for High-Fidelity Simulation Study of
Crash Detection Algorithms
- Authors: Ahura Jami, Mahdi Razzaghpour, Hussein Alnuweiri, Yaser P. Fallah
- Abstract summary: We present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles.
We decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario.
We analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics.
- Score: 2.064612766965483
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Developing safety and efficiency applications for Connected and Automated
Vehicles (CAVs) require a great deal of testing and evaluation. The need for
the operation of these systems in critical and dangerous situations makes the
burden of their evaluation very costly, possibly dangerous, and time-consuming.
As an alternative, researchers attempt to study and evaluate their algorithms
and designs using simulation platforms. Modeling the behavior of drivers or
human operators in CAVs or other vehicles interacting with them is one of the
main challenges of such simulations. While developing a perfect model for human
behavior is a challenging task and an open problem, we present a significant
augmentation of the current models used in simulators for driver behavior. In
this paper, we present a simulation platform for a hybrid transportation system
that includes both human-driven and automated vehicles. In addition, we
decompose the human driving task and offer a modular approach to simulating a
large-scale traffic scenario, allowing for a thorough investigation of
automated and active safety systems. Such representation through Interconnected
modules offers a human-interpretable system that can be tuned to represent
different classes of drivers. Additionally, we analyze a large driving dataset
to extract expressive parameters that would best describe different driving
characteristics. Finally, we recreate a similarly dense traffic scenario within
our simulator and conduct a thorough analysis of various human-specific and
system-specific factors, studying their effect on traffic network performance
and safety.
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