An Asymmetric-Anticipation Car-following Model in the Era of Autonomous-Connected and Human-Driving Vehicles

Ammar Jafaripournimchahi,Lu Sun,Wusheng Hu,Haneen Farah

doi:10.1155/2020/8865814

Ammar Jafaripournimchahi, Lu Sun + Show 2 more

Open Access

https://doi.org/10.1155/2020/8865814

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Abstract

Herein, we explored the impact of anticipation and asymmetric driving behavior on vehicle’s position, velocity, acceleration, energy consumption, and exhaust emissions of CO, HC, and NOx in mixed traffic flow. We present an asymmetric-anticipation car-following model (AAFVD) considering the motion information from two direct preceding vehicles (i.e., human-driving (HD) and autonomous and connected (AC) vehicles platoon) via wireless data transmission. The linear stability approach was used to evaluate the properties of the AAFVD model. Our simulations revealed that the drivers’ anticipation factor using the motion information from two direct preceding vehicles in connected vehicles environment can effectively improve traffic flow stability. The vehicle’s departure and arrival process while passing through a signal lane with a traffic light considering the anticipation and asymmetric driving behavior, and the motion information from two direct preceding vehicles was explored. Our numerical results demonstrated that the AAFVD model can decrease the velocity fluctuations, energy consumption, and exhaust emissions of vehicles in mixed traffic flow system.

Highlights

We propose an asymmetric-anticipation full velocity difference (AAFVD) model to study the impacts of asymmetric characteristic and anticipation driving behavior on mixed traffic flow dynamics
To contribute to the state of the art in traffic flow theory, we proposed the AAFVD model based on the FVD model with an anticipation optimal velocity function considering the reflection of vehicular gap and velocity changes of two preceding vehicles using the V2V communication technology
Our results revealed that high acceleration and deceleration will not appear, and considering the anticipation driving behavior for designing the control strategy of mixed traffic systems can increase positive traffic metrics and decrease energy consumption and CO, HC, and NOx emissions

Summary

Introduction

Many existing human-driving car-following models have introduced to capture the process of the drivers’ behavior individually, traveling in various contexts on the road without any overtaking. There would be a mixed traffic flow of human-driving and autonomous vehicles on the roads. A nonlinear version of safety distances models was proposed by Newell [23] to solve the deficiency of existing car-following models in extremely low densities, by presuming that the following vehicle’s velocity has a nonlinear relationship with the vehicular gap. Bando et al [24] proposed a remarkable car-following model called the OVM based on the assumption that the optimal velocity of the following vehicle was determined by his/her own vehicular gap to modify the shortcoming in Newell’s model. Based on the asymmetric driving assumption, Gong et al [28] proposed the AFVD model by taking two different sensitivity parameters into account, as shown in the following equation: Δxn(t)􏼁 −. With the development of ITS, the driver can acquire information from the preceding vehicles which affects the

Limitations

The Car-following Model

Simulations

Concluding Remarks