Leveraging Vehicle Connectivity and Autonomy to Stabilize Flow in Mixed Traffic Conditions: Accounting for Human-driven Vehicle Driver Behavioral Heterogeneity and Perception-reaction Time Delay

TL;DR

This paper proposes a controller for connected autonomous vehicles to stabilize mixed traffic flow by accounting for human driver heterogeneity and reaction delays, demonstrating improved traffic stability and safety.

Contribution

It introduces a novel CAV controller design that explicitly considers human driver variability and perception delays, enhancing traffic stabilization in mixed environments.

Findings

CAVs can stabilize fewer HDVs when driver heterogeneity and delays are considered.

The designed controller improves traffic stability and safety in mixed traffic conditions.

Considering driver behavior uncertainties is crucial for effective CAV control strategies.

Abstract

The erratic nature of human driving tends to trigger undesired waves that amplify as successive driver reactions propagate from the errant vehicle to vehicles upstream. Known as phantom jams, this phenomenon has been identified in the literature as one of the main causes of traffic congestion. This paper is based on the premise that vehicle automation and connectivity can help mitigate such jams. In the paper, we design a controller for use in a connected and autonomous vehicle (CAV) to stabilize the flow of human-driven vehicles (HDVs) that are upstream of the CAV, and consequently to lower collision risk in the upstream traffic environment. In modeling the HDV dynamics in the mixed traffic stream, we duly consider HDV driver heterogeneity and the time delays associated with their perception reaction time. We can find that the maximum number of HDVs that a CAV can stabilize is lower when human drivers potential time delay and heterogeneity are considered, compared to the scenario where such are not considered. This result suggests that heterogeneity and time delay in HDV behavior impairs the CAVs capability to stabilize traffic. Therefore, in designing CAV controllers for traffic stabilization, it is essential to consider such uncertainty-related conditions. In our demonstration, we also show that the designed controller can significantly improve both the stability of the mixed traffic stream and the safety of both CAVs and HDVs in the stream. The results are useful for real-time calibration of the model parameters that characterize HDV movements in the mixed stream.