# A New Microscopic Traffic Model Using a Spring-Mass-Damper-Clutch System

## TL;DR

This paper introduces a novel microscopic traffic model based on a spring-mass-damper-clutch system that captures vehicle interactions and can be scaled for macroscopic traffic analysis, with real-time parameter identification for vehicle control.

## Contribution

The paper proposes a physically interpretable microscopic traffic model using a spring-mass-damper-clutch system and develops an efficient online parameter estimation algorithm, enhancing traffic flow understanding and vehicle control.

## Key findings

- Model captures ego vehicle resistance and impact on lead vehicle.
- The proposed model is scalable for macroscopic traffic analysis.
- The PRLS-IQR algorithm enables real-time, stable parameter estimation.

## Abstract

Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle's resistance to large relative speed and deviation from a (driver and speed dependent) desired relative distance when following the lead vehicle. Comparing to existing car following (CF) models, this model offers physically interpretable insights on the underlying CF dynamics, and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model therefore has great scalability so that multiple mass-spring-damper-clutch system can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated in advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable so it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated.