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

arXiv: https://arxiv.org/abs/2208.05540 · HTML: https://mlsanity.com/paper/2208.05540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications](https://mlsanity.com/paper/2005.13781/) (2020)
- [Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks](https://mlsanity.com/paper/1809.02678/) (2018)
- [Gaussian Process based Stochastic Model Predictive Control for Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2111.07162/) (2021)
- [Impact of Information Flow Topology on Safety of Tightly-coupled Connected and Automated Vehicle Platoons Utilizing Stochastic Control](https://mlsanity.com/paper/2203.15772/) (2022)
- [Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application](https://mlsanity.com/paper/1808.06463/) (2018)
- [Finite State Markov Modeling of C-V2X Erasure Links For Performance and Stability Analysis of Platooning Applications](https://mlsanity.com/paper/2111.07173/) (2021)

## Built on by (most cited first, 3 of 3)

- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems](https://mlsanity.com/paper/2212.12819/) (2022)

## Abstract (arXiv)

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.