The improvement of energy performance for platoons of autonomous connected vehicles is one of the major challenges the road transport sector is facing with. To this aim, this work addresses and solves the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon via a novel Eco-Driving Control Architecture able to optimize its energy consumption performance while ensuring the fulfillment of the optimal leader tracking trajectory. Specifically, it consists of a Nonlinear Model Predictive Control (NMPC) strategy, driving the leader motion and computing the optimal ecological trajectory to be imposed on the whole platoon, and a novel distributed exponentially-stable robust PID-like protocol, driving the follower vehicles motion for achieving a precise leader-tracking with a desired transient behavior as required for the accurate implementation of the energy-saving control. The exponential stability of the overall vehicular network is analytically proven with the Lyapunov theory and the derived robust stability conditions allow the proper tuning of the control gains on the basis of the desired decay rate. The efficiency of the proposed approach is corroborated via the high-fidelity Mixed Traffic Simulator (MiTraS) co-simulation platform under different operative scenarios and a wide uncertainty range for the vehicles parameters. Simulation results confirm how the proposed architecture ensures the eco-driving behaviour for the whole vehicles platoon.

Connected Autonomous Vehicles (CAVs) are going to share the road environment with human drivers until their full market deployment achievement. In this context, this paper tackles and solves the problem of decentralized crossing at unsignalized intersections for mixed traffic flows, composed of both connected human-driven vehicles (CHVs) and CAVs. To this aim, a novel cooperative fully-distributed control protocol for CAVs is proposed, augmenting the Adaptive Cruise Control (ACC) action based on proximity sensors measurements, with an additional networked protocol exploiting Vehicle-to-Vehicle (V2V) information for the cooperative evaluation of the Time-to-Intersection of all incoming vehicles within the communication range. This further collaborative action automatically adapts the CAVs motion at intersection avoiding both collisions with other vehicles and intersection deadlocks, while improving the intersection throughput. Safety and mobility performances are evaluated via the purposely designed Virtual Testing platform - Mixed Traffic Simulator (MiTraS)- combining the Matlab/Simulink environment with the SUMO traffic simulator. The analysis is carried out for an exemplary two-lane four-way unsignalized intersection considering both different traffic demands and different CAVs penetration rates. The unavoidable presence of variable delays in information delivery due to the wireless communication network has been also explicitly accounted for in both control design and validation. The extensive simulation analysis confirms how the inclusion of CAVs, equipped with the proposed control algorithm, within the mixed traffic flow strongly improves both the safety and mobility performances of the intersection. Lastly, we disclose how the benefits of the proposed controller increase with CAV penetration.

This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.