The Usage-Based Insurance paradigm, which is receiving a lot of attention in recent years, envisages computing the car policy premium based on accident risk probability, evaluated observing the past driving history and habits. However, Usage-Based Insurance strategies are usually based on simple empirical decision rules built on travelled distance. The development of intelligent systems for smart risk prediction using the stored overall driving behaviour, without the need of other insurance or socio-demographic information, is still an open challenge. This work aims at exploring a comprehensive machine learning-based approach solely based on driving-related data of private vehicles. The anonymized dataset employed in this study is provided by the telematics company UnipolTech, and contains space/time densely measured data related to trips of almost 100000 vehicles uniformly spread on the Italian territory, recorded every 2 km by on-board telematics fix devices (black boxes), from February 2018 to February 2020. An innovative feature engineering process is proposed, with the aim of uncovering novel informative quantities able to disclose complex aspects of driving behaviour. Recent and powerful learning techniques are explored to develop advanced predictive models, able to provide a reliable accident probability for each vehicle, automatically managing the critical imbalance intrinsically peculiar this kind of datasets.

Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over one or more others. Accordingly, algorithms for active preference learning have been developed to exploit this specific information in constructing a surrogate of the objective function. This surrogate is then used to define an acquisition function that suggests new decision vectors to search for the optimal solution iteratively. Based on this idea, our approach aims to extend active preference learning algorithms to leverage further information effectively, which can be obtained in reality, such as: a five-point Likert-type scale for the outcomes of the preference query (i.e., the preference can be described not only as “this is better than that” but also as “this is much better than that”), or multiple outcomes for a single preference query with possible additive information on how certain the outcomes are. The validation of the proposed algorithm is done through some standard benchmark functions, and, in practice, through tuning parameters for robot sealing and human–robot collaboration experiments, showing a promising improvement with respect to the state-of-the-art algorithm in the same context.

The main objective of this paper is to determine the feasibility of designing a driver drunkenness detection system based on the dynamic analysis of a subject's pupillary light reflex (PLR). This involuntary reaction is widely utilized in the medical field to diagnose a variety of diseases, and in this paper, the effectiveness of such a method to reveal an impairment condition due to alcohol abuse is evaluated. The test method consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes; for extracting the pupil size profiles from the video sequences, a two-step methodology is described, where in the first phase, the iris/pupil search within the image is performed, and in the second stage, the image is cropped to perform pupil detection on a smaller image to improve time efficiency. The undesired pupil dynamics arising in the PLR are defined and evaluated; a spontaneous oscillation of the pupil diameter is observed in the range [0, 2] Hz and the accommodation reflex causes pupil constriction of about 10% of the iris diameter. A database of pupillary light responses is acquired on different subjects in baseline condition and after alcohol consumption, and for each one, a first-order model is identified. A set of features is introduced to compare the two populations of responses and is used to design a support vector machine classifier to discriminate between “Sober” and “Drunk” states.

The increasing complexity of computing architectures is pushing for novel Dynamic Thermal Management (DTM) techniques. Accordingly, more accurate power and thermal models are required. In this work, we propose a thermal controller based on a constrained extremum-seeking algorithm, enabling resource allocation optimization under specific thermal constraints. This approach comes with many advantages. First, the controller does not require any model of the system, dropping the need for a complex and potentially imprecise estimation phase. Second, it allows the control of derived measurements. We show how this may positively impact on the CPU reliability.

Standard control design for robot implicit force control is a typical example of model-based regulator synthesis. This paper proposes a method to improve closed-loop performance of standard model-based controllers for robot implicit force control in terms of closed-loop model matching, between desired and achieved closed-loop behaviour. To this end, a data-driven controller design method, based on the Virtual Reference Feedback Tuning (VRFT) approach, is introduced. Advantages in terms of robustness with respect to unknown environment stiffness are discussed and demonstrated. The effectiveness of the proposed control strategy is experimentally validated on an industrial robot equipped with a force sensor.

Electric vehicles (EVs) are recognized to be an effective way to reduce global warming pollution. Therefore, there is an increasing interest and effort in developing new ideas and technology to spread the use of EVs as an alternative to conventional cars. However, the consumers' perception of EVs, especially concerning their limited and not-fully predictable range, is still of low reliability, thus the number of sales is increasing slower than expected. In this paper, a software tool for non-EVs owners is presented, with the aim of profiling the drivers and show which EV alternative, if any, would be suitable for each case. The final goal is to encourage electric mobility against range anxiety. The whole analysis is carried out by using experimental data collected from some users in Milan, Italy.

This paper addresses the issue of data-driven online velocity optimization of an autonomous sailboat. Autonomous sailboats represent an ideal for long range and duration reconnaissance missions. Sailboat control is a challenging control task; sailboats are characterized by a number of control variables, all of which affect the ship trajectory and state in a highly nonlinear fashion. In this paper, a path-following automatic sailboat controller is presented. The control system has two main components: a heading control, acting on the rudder, and a velocity optimizer, acting on the sails. The optimizer is based on a modified extremum seeking approach. This paper also derives a first-principle-based 4 DoF sailboat model that is experimentally validated and used to guide the design and tuning of the control system. In fact, the control system is first tuned and validated in simulation. The simulation environment enables the comparison of the proposed model against a theoretical benchmark and a state-of-the-art controller. The analysis reveals that the proposed control system achieves near-optimal performance and considerably outperforms the state-of-the-art solution. Finally, the controller is tested and validated on an instrumented scale model.

Range extenders (REs) increase the driving range of electric vehicles (EVs) at the price of additional weight and encumbrance, which are unnecessary in normal urban usage. An innovative concept of an extended range EV is studied here, i.e., an EV that can exploit a rental RE only when necessary to complete a mission. The energy management system not only dispatches power between a battery and an RE but also decides whether or not and when to rent the RE. This paper investigates the optimal control policy that minimizes the monetary cost attained by the user. A simulation analysis carried out in a representative set of scenarios demonstrates the interest of this formulation and shows that mixed-integer convex programming (MI-CP) attains high accuracy in a fraction of the time required by dynamic programming, with a negligible loss of performance. In a real-time framework, the MI-CP core is fed by a forecast of the mission and integrated with a power dispatch strategy that optimizes local performance. The simulation study shows that the resulting policy approaches the global optimum.

The free-floating car sharing model is a recently introduced vehicle rental model, which allows customers to return the car anywhere within the operation area, without relying on depot stations. Driven by the flexibility of such a model, the popularity of car sharing has increased rapidly during the last years. However, some critical issues still arise when a user needs to make plans of vehicle usage, since no information is available on future vehicle locations. In this paper, the Vehicle Distance Prediction (VDP) approach is proposed, aimed to predict the distance of the nearest available vehicle at a given future instant. This technique shows great potential also for the service manager, e.g. vehicles could be moved in advance by the staff to balance the fleet distribution. The effectiveness of the proposed prediction approach is assessed on a real dataset taken from a car sharing service in Milan, Italy.

In this paper, a data-driven approach is proposed to tune fixed-order controllers for unknown stable LTI plants in a mixed-sensitivity loop-shaping framework. The method requires a single set of input-output samples and it is based on convex optimization techniques; moreover, it asymptotically guarantees the internal stability of the closed-loop system. The effectiveness of the method is illustrated with application to the control of an active suspension system.