The paper addresses the leader tracking problem for a platoon of connected autonomous vehicles in the presence of both homogeneous time-varying parameter uncertainties and Vehicle-to-Vehicle time-varying communication delay. To this aim, leveraging the Multi-Agent Systems (MAS) framework, a novel distributed robust PID control is proposed. The stability of the cohesive formation is analytically proved with a Lyapunov-Krasovskii approach by exploiting the descriptor transformation for time-delayed systems of neutral type. The delay-dependent robust stability conditions are expressed as a set of Linear Matrix Inequalities allowing the proper tuning of the proportional, integral and derivative actions implemented on each of the vehicles within the fleet. Extensive simulation analysis in different driving scenarios confirms the effectiveness of the theoretical derivation.

The introduction of active safety systems and advanced driver assistance systems has enhanced the control authority over the vehicle dynamics through specialized actuators, enabling, for instance, independent wheel torque control. During emergency situations, these systems step in to aid the driver by limiting vehicle response to a stable and controllable range of low longitudinal tire slips and slip angles. This approach makes vehicle behavior predictable and manageable for the average human driver; however, it is conservative in case of driving automation. In fact, past research has shown that exceeding the operational boundaries of conventional active safety systems enables trajectories that are otherwise unattainable. This paper presents a nonlinear model predictive controller (NMPC) for path tracking (PT), which integrates steering, front-to-total longitudinal tire force distribution, and direct yaw moment actuation, and can operate beyond the limit of handling, e.g., to induce drift, if this is beneficial to PT. Simulation results of emergency conditions in an intersection scenario highlight that the proposed solution provides significant safety improvements, when compared to the concurrent operation of PT algorithms and the current generation of vehicle stability controllers.

This paper presents a novel integrated control architecture for automotive battery management systems (BMSs). The primary focus is on estimating the state of charge (SoC) and the state of health (SoH) of a battery pack made of sixteen parallel-connected modules (PCMs), while actively balancing the system. A key challenge in this architecture lies in the interdependence of the three algorithms, where the output of one influences the others. To address this control problem and obtain a solution suitable for embedded applications, the proposed algorithms rely on an equivalent circuit model. Specifically, the SoCs of each module are computed by a bank of extended Kalman filters (EKFs); with respect to the SoH functionality, the internal resistances of the modules are estimated via a linear filtering approach, while the capacities are computed through a total least squares algorithm. Finally, a model predictive control (MPC) was employed for the active balancing. The proposed controller was calibrated with Samsung INR18650-20R lithium-ion cells data. The control system was validated in a simulation environment through typical automotive dynamic scenarios, in the presence of measurement noise, modeling uncertainties, and battery degradation.

Driver behaviour and distraction have been identified as the main causes of rear end collisions. However a promptly issued warning can reduce the severity of crashes, if not prevent them completely. This paper proposes a Forward Collision Warning System (FCW) based on information coming from a low cost forward monocular camera for low end electric vehicles. The system resorts to a Convolutional Neural Network (CNN) and does not require the reconstruction of a complete 3D model of the surrounding environment. Moreover a closed-loop simulation platform is proposed, which enables the fast development and testing of the FCW and other Advanced Driver Assistance Systems (ADAS). The system is then deployed on embedded hardware and experimentally validated on a test track.

The growing dependency of human activities on software technologies is leading to the need for designing more and more accurate testing techniques to ensure the quality and reliability of software components. A recent literature review of software testing methodologies reveals that several new approaches, which differ in the way test inputs are generated to efficiently explore systems behaviour, have been proposed. This paper is concerned with the challenge of automatically generating test input sets for Event-Driven Systems (EDS) for which neither source code nor specifications are available, therefore we propose an innovative fully automatic testing with model learning technique. It basically involves active learning to automatically infer a behavioural model of the System Under Test (SUT) using tests as queries, generates further tests based on the learned model to systematically explore unseen parts of the subject system, and makes use of passive learning to refine the current model hypothesis as soon as an inconsistency is found with the observed behaviour. Our passive learning algorithm uses the basic steps of Evidence-Driven State Merging (EDSM) and introduces an effective heuristic for choosing the pair of states to merge to obtain the target machine. Finally, the effectiveness of the proposed testing technique is demonstrated within the context of event-based functional testing of Android Graphical User Interface (GUI) applications and compared with that of existing baseline approaches.