Abstract
Modeling for mixed autonomy traffic: a control-based approach
Maria Laura Delle Monache
University of California, Berkeley (UC Berkeley)
In this talk we will give an overview of different approaches to model mixed traffic encompassing human driven, driver-assist and automated vehicles. We will show how different modeling approaches will affect traffic representation and its control.
We will show microscopic, macroscopic and multi-scale modeling approaches and for each of them we will give a glimpse on how the control can be designed and actuated.
In particular, we will focus on a class of second order microscopic models in which the different vehicles have different dynamics and on a class of coupled PDE-ODE models describing the interaction of autonomous vehicles with the surrounding traffic. The traffic flow is described with a scalar conservation law while each autonomous vehicle trajectory is described
by an ODE. The presence of the vehicles can induce a moving bottleneck, hindering traffic flow or act as tracer vehicles in the flow to collect measurements along their trajectory to estimate the bulk flow.
We will show microscopic, macroscopic and multi-scale modeling approaches and for each of them we will give a glimpse on how the control can be designed and actuated.
In particular, we will focus on a class of second order microscopic models in which the different vehicles have different dynamics and on a class of coupled PDE-ODE models describing the interaction of autonomous vehicles with the surrounding traffic. The traffic flow is described with a scalar conservation law while each autonomous vehicle trajectory is described
by an ODE. The presence of the vehicles can induce a moving bottleneck, hindering traffic flow or act as tracer vehicles in the flow to collect measurements along their trajectory to estimate the bulk flow.