Data-Driven Adaptive Automated Driving Model in Mixed Traffic

Abstract

The interplay of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HDVs) in mixed traffic environments is often presumed to influence the behaviour of the other, and the dynamic impacts of such interplay on traffic flows is a critical aspect absent in most existing studies. This study employs a data-driven optimization approach to model the driving behaviour of Connected Automated Vehicles (CAV) in mixed traffic and investigate the impact of CAVs on overall traffic performance. Specifically, considering a scenario of a gradual increase in the penetration of CAVs in the conventional traffic stream, currently dominated by Human-driven vehicles (HDV), four possible car-following configurations are identified where a CAV has to behave differently. Regarding such configurations, existing car-following and lane-changing models of CAVs are tuned using a Lipschitzian optimization algorithm and a local search method with data obtained from the WAYMO Open Dataset. The developed driving model of CAVs is used to simulate mixed traffic on a freeway section attached to an on-ramp, which often induces traffic bottlenecks. Under varying market penetration of CAVs, traffic performances, including travel time, throughput, and string stability, are compared with conventional traffic. The findings suggest significant improvements at a network level, for example, by delaying and dampening shockwaves. However, on an individual level, CAVs feel hindered by the slower-moving HDVs.

Review and Computational Study on Practicality of Derivative-Free DIRECT-Type Methods

“…In addition, these algorithms have been employed to optimize shifting strategies for multi-gear and multi-mode parallel plug-in hybrid electric vehicles, ultimately contributing to the advancement of efficient and secure transportation systems. The relevant literature includes Bouadi et al (2022); Ramsahye et al (2023); Wang et al (2023) research in this area. Additionally, DIRECT-type algorithms have shown successful applications in structural engineering (Jin et al, 2023), particularly in truss optimization (Mockus et al, 2017) and optimizing the load capacity of slider bearings in thermohydrodynamic lubrication (Wang et al, 2011).…”