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

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.

A Density-Based and Lane-Free Microscopic Traffic Flow Model Applied to Unmanned Aerial Vehicles

“…Many of the classic models are 1-lane models. Among the classics are the Optimal Velocity Model (OVM) [15], Full Velocity Difference Model (FVDM) [10], and Intelligent Driver Model (IDM) [16] whereas [51] is a more recent example. However, it is possible to extend these to multi-lane models by use of a lane change model such as MOBIL which dictates the rule of when it is safe and beneficial for a vehicle to change lanes [11].…”

“…where the last equality is due to (51) and knowing v eq = V 0 . Therefore, the bigger the difference between V 0 and V n , the faster the convergence will be to the equilibrium point.…”

A flock-like two-dimensional cooperative vehicle formation model based on potential functions

“…Potential fields were applied in recent studies to control multi-vehicles motion Yi et al (2020). Li et al (2017) applied the spring-mass system theory to establish the car-following model based on the elastic potential field, and Li et al (2019) considered time delay and stability analysis in the spring-mass-damper-clutch system. Bang and Ahn (2017) used swarm intelligence as the control strategy and spring-mass-damper system theory as agent interaction rules.…”

A Mechanical System Inspired Microscopic Traffic Model: Modeling, Analysis, and Validation

“…To obtain a CF model with better physical interpretability, in our prior work [35], we developed a mass-spring-damper- clutch traffic model that captures many natural driving behaviors in car-following dynamics. Specifically, a mechanical spring between two masses (the lead and ego vehicles) resembles the ego vehicle's tendency to accelerate/decelerate when the relative distance to the preceding vehicle is too large/small; a mechanical damper is used to characterize the ego vehicle's tendency to follow a similar speed as the preceding vehicle, and a mechanical clutch system is to model driver's delayed actions due to the reaction time.…”