CAREER: Socially-Aware Control of Autonomy: Reshaping Urban Mobility in Traffic Networks with Mixed Vehicle Autonomy

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As cities grow everywhere, and urban roadways become overburdened, efficient strategies are required for improving urban mobility. With the emergence of autonomous cars, there is an opportunity to reclaim urban mobility provided that a proactive control and planning approach is taken. In this project, our goal is to develop fundamental theory and domain-driven techniques for leveraging the opportunities that autonomous cars provide to achieve mobility-efficient smart cities. We consider mixed-autonomy traffic networks which are road networks that are shared by human-driven and autonomous cars. We will develop a set of algorithms for socially-aware control of autonomous cars in these systems. More precisely, we develop control algorithms that take into account the social implications of the co-existence of human-driven and autonomous cars and guarantee overall societal good. Our project enables the transformation from the current reality to the brighter future, in which autonomous cars harmoniously interact with the road and network traffic control systems to increase mobility. To unlock the mobility potentials of autonomous cars, a key control challenge is to account for how humans adapt and respond to autonomous cars’ actions such as routing decisions. We focus on travelers’ routing decisions and develop design and control algorithms that induce efficient routing decisions. We use routing games for modeling travelers’ route choices over traffic networks and tackle research challenges associated with controlling the core components of a mixed-autonomy network. (i) We will develop algorithms for altruistic control of autonomous cars’ routing decisions, that is, autonomous cars that are routed in the favor of society. (ii) We will develop pricing algorithms that affect humans' routing decisions such that the network is steered towards desirable states. (iii) Finally, we will develop planning strategies for network topology design and capacity regulation that induce efficient routing decisions by travelers. Our proposed control strategies will also be validated through extensive real-world experiments and traffic simulations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.