A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections

Abstract

We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time.

Survey on Artificial Intelligence for Vehicles

“…As AI techniques develop from ANI, to AGI and ASI, it will help the development of vehicles in different scales. In the intelligent vehicle field, as AI develops, it will make vehicles more and more intelligent, from L1 to L4 as defined by SAE standards [41]. For the connected vehicle field, AI techniques will help the connected vehicle technology from in-car computation to cloud computation and realize real-time communication between vehicles and roadside units.…”

“…(3) Intelligent decision technology, including risk situation modeling technology, risk warning and control priority division, multi-objective collaborative technology, vehicle trajectory planning, driver diversity analysis, human-computer interaction. (4) The vehicle control technology [41,56], which includes longitudinal motion control system based on the drive and braking system, lateral motion control based on steering system, vertical motion control based on driving/braking/steering/chassis integrated control and suspension, and at the same time, it can use communication and vehicle sensor to achieve team collaboration and cooperative vehicle. (5) Data platform technology which includes nonrelational database schema, efficient data storage and retrieval, association analysis and deep mining of large data cloud operating system and information security mechanism.…”

Decentralized optimal coordination of connected and automated vehicles for multiple traffic scenarios

“…Finally, the last assumption is imposed to enhance safety awareness. However, it could be modified appropriately, if necessary, as discussed by Malikopoulos et al (2018).…”

“…Several papers have also focused on multi-objective optimization problems using a receding horizon control solution either in centralized or decentralized approaches; see Kamal et al (2013); Makarem et al (2013); Qian et al (2015). Lately, a decentralized optimal control framework was presented for coordinating online CAVs in different transportation scenarios, yet without considering state and control constraints; see Ntousakis et al (2016); Zhao et al (2019), or without considering rear-end collision avoidance constraint; see Malikopoulos et al (2018). A detailed discussion of the research efforts in this area can be found in recent survey papers (Rios-Torres and Malikopoulos, 2017;Guanetti et al, 2018).…”

Optimal Path Planning for Connected and Automated Vehicles at Urban Intersections

“…There is a number of ways to determine t f i for each CAV i. For example, we may impose a strict first-in-firstout queueing structure [27], [29], [32], [36], where each CAV must enter the merging zone in the same order it entered the control zone. The policy, which determines the time t f i that each CAV i exits the control zone, is the result of an upper-level optimization problem which can aim at maximizing the throughput at the intersection.…”