Centralized control in mixed traffic: Energy efficiency and robustness under partial controllability

Abstract

With the increasing deployment of autonomous transportation systems, longitudinal vehicle control has so far mostly been addressed using decentralized feedback-based approaches such as Adaptive Cruise Control (ACC). These methods offer low computational complexity and inherent robustness, and are widely deployed in production vehicles as part of Advanced Driver Assistance Systems (ADAS). However, in complex traffic situations, such as urban intersection scenarios, decentralized approaches often cannot fully exploit the available degrees of freedom. Fully autonomous traffic environments therefore motivate centralized control concepts that leverage system-level controllability and observability to coordinate vehicle behavior at intersections.This paper presents a centralized longitudinal control approach designed for operation in both fully autonomous and mixed traffic at urban intersections and compares it to a decentralized reference in terms of travel speed and energy consumption. The centralized formulation addresses mixed traffic conditions by operating under partial controllability and limited predictability of non-autonomous vehicles. While fully autonomous scenarios allow the approach to exploit system-wide controllability, its behavior in mixed traffic is shaped by the specific traffic situation and the accuracy of vehicle motion predictions. The results illustrate how partial loss of controllability influences system-level performance.