Antonio Tota

This paper proposes a calibration-oriented framework for cooperative adaptive cruise control based on a linear quadratic regulator formulation. A simulation-based architecture is developed by integrating the controller with a nonlinear longitudinal platoon model that explicitly accounts for actuator saturation and tyre–road friction limits, enabling the analysis of platoon behaviour under realistic operating conditions. A systematic offline calibration methodology is introduced based on multidimensional performance maps, relating key performance indicators associated with collision avoidance, comfort, and energy efficiency to controller and spacing-policy tuning parameters. The map-based approach enables a structured exploration of competing objectives and provides a quantitative assessment of controller sensitivity. The results show that the proposed framework can identify calibration regions that preserve collision-free operation in safety-critical manoeuvres while maintaining satisfactory tracking and comfort-related performance. In addition, the off-nominal model parameters analysis confirms that the proposed calibration approach remains effective under heterogeneous operating conditions, including vehicle parametric variation of mass, rolling resistance coefficient and drag. Overall, the results support the use of the proposed methodology as a practical tool for robust and performance-oriented controller calibration.

Heavy vehicle rollover plays a pivotal role in road safety scenarios. Numerous researchers addressed the topic, with particular focus on drivers related injuries. Considering the same and other connected implications, the necessity for techniques able to estimate and predict overturning eventualities appears evident. Different methodologies were explored, with notable achievements obtained by neural network-based algorithms. At the same time, their heavy requirements in terms of data needs to be addressed to allow practical applications in terms of time and costs. Consequently, exploring the interaction between simulation and experimental data becomes extremely important, motivating the methodology proposed by this paper. In details, an heavy vehicle model was designed in IPG Carmaker, while experimental data on its physical alter ego were acquired. This led to the generation of a synthetic dataset and the collection of an empirical one. Both were used to define a Long Short-Term Memory architecture, with a dual purpose. First, as typical rollover indicator, estimate the vehicle roll angle. Second, compare the performance of the neural networks, aiming to obtain at least the same order of magnitude in terms of RMSE, MSE and MAE. The goal was to demonstrate that synthetic data can not only be used in combination with real data, but also as substitutes able to address time and cost constraints inevitably linked to the latter, allowing more efficient experiments for overtipping prevention.

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability.

This paper aims at investigating powertrain behaviour, especially in transient dynamic responses, using a nonlinear truck vehicle dynamic model with a parallel hybrid configuration. A power split control was designed to achieve the desired drivability performance, with a focus on NOx emissions. The controller was characterized by high-level model-based logic used to elaborate the total powertrain torque required, and a low-level allocation strategy for splitting power between the engine and the electric motor. The final task was to enhance vehicle drivability based on driver requests, with the goal of reducing—in a hybrid configuration—transient diesel engine emissions when compared to a conventional pure thermal engine powertrain. Different parameters were investigated for the assessment of powertrain performance, in terms of external input disturbance rejection and NOx emissions reduction. The investigation of torque allocation performance was limited to the simulation of a Tip-in manoeuvre, which showed a satisfying trade-off between vehicle drivability and transient emissions.