Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms

TL;DR

This paper introduces an augmented driver behavior model for high-fidelity traffic simulation, enabling detailed evaluation of crash detection algorithms in mixed human and automated vehicle environments.

Contribution

It presents a modular, human-interpretable simulation platform with data-driven driver behavior modeling for large-scale traffic scenarios.

Findings

Enhanced simulation accuracy for driver behavior.

Insights into factors affecting traffic safety and performance.

Validation of models against real driving data.

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.