Luca Davoli

@article{gaother:2025:cea,

title = {An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes},

author = {Martina Galaverni and Giulia Oddi and Luca Preite and Laura Belli and Luca Davoli and Ilaria Marchioni and Margherita Rodolfi and Federico Solari and Deborah Beghè and Tommaso Ganino and Giuseppe Vignali and Gianluigi Ferrari},

doi = {10.1016/j.compag.2024.109660},

issn = {0168-1699},

year  = {2024},

date = {2024-12-01},

urldate = {2025-01-01},

journal = {Computers and Electronics in Agriculture},

volume = {229},

pages = {109660},

abstract = {The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as Agriware, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor on-field parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.},

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}

@article{suoldastnofe:2024:access,

title = {CSI-RSRP-Based Unnecessary Handover Mitigation Through Linear Regression in Dynamic 5G NR Environments},

author = {Sunil Mathew and Eleonora Oliosi and Luca Davoli and Nicolò Strozzi and Andrea Notari and Gianluigi Ferrari},

doi = {10.1109/ACCESS.2024.3451483},

issn = {2169-3536},

year  = {2024},

date = {2024-08-29},

urldate = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {121808-121821},

abstract = {5G New Radio (NR), introduced in 2019 in the 3rd Generation Partnership Project (3GPP) Release 15, has become the global radio standard for 5G networks. Because of the presence of an increasing number of available 5G gNodeBs (gNBs), HandOver (HO) management is crucial, especially in terms of Quality of Service (QoS) and Quality of Experience (QoE) perceived by a User Equipment (UE). Unnecessary HandOvers (UHOs) cause latency peaks (on the order hundreds of milliseconds) and multiple throughput drops in 5G communications. In this paper, we first carry out an experimental campaign to investigate the behaviour of latency and throughput in correspondence to UHOs. Then, on the basis of a Matlab-based 5G NR DownLink (DL) transmission simulator, we propose an innovative linear regression-based algorithm to avoid UHOs, which relies on Channel State Information-Reference Signal Received Power (CSI-RSRP) measurements.},

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}

@article{madabegacasgrafe:2024:sensors,

title = {IoT-Based Assessment of a Driver’s Stress Level},

author = {Veronica Mattioli and Luca Davoli and Laura Belli and Sara Gambetta and Luca Carnevali and Andrea Sgoifo and Riccardo Raheli and Gianluigi Ferrari},

doi = {10.3390/s24175479},

issn = {1424-8220},

year  = {2024},

date = {2024-08-23},

urldate = {2024-01-01},

journal = {Sensors},

volume = {24},

number = {17},

abstract = {Driver Monitoring Systems (DMSs) play a key role in preventing hazardous events (e.g., road accidents) by providing prompt assistance when anomalies are detected while driving. Different factors, such as traffic and road conditions, might alter the psycho-physiological status of a driver by increasing stress and workload levels. This motivates the development of advanced monitoring architectures taking into account psycho-physiological aspects. In this work, we propose a novel in-vehicle Internet of Things (IoT)-oriented monitoring system to assess the stress status of the driver. In detail, the system leverages heterogeneous components and techniques to collect driver (and, possibly, vehicle) data, aiming at estimating the driver’s arousal level, i.e., their psycho-physiological response to driving tasks. In particular, a wearable sensorized bodice and a thermal camera are employed to extract physiological parameters of interest (namely, the heart rate and skin temperature of the subject), which are processed and analyzed with innovative algorithms. Finally, experimental results are obtained both in simulated and real driving scenarios, demonstrating the adaptability and efficacy of the proposed system.},

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}

@article{padafe:2024:taes,

title = {Wi-Fi-Based Real-Time UAV Localization: A Comparative Analysis Between RSSI-Based and FTM-Based Approaches},

author = {Emanuele Pagliari and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/TAES.2024.3433829},

issn = {1557-9603},

year  = {2024},

date = {2024-07-25},

urldate = {2024-01-01},

journal = {IEEE Transactions on Aerospace and Electronic Systems},

volume = {60},

number = {6},

pages = {8757-8778},

abstract = {Wi-Fi connectivity for localization purposes has been used for several years in the Internet of Things (IoT) context, where the (general) static nature of IoT devices allows to approximately localize them in known environments with low effort and implementation costs. While the accuracy of Wi-Fi localization for IoT applications can be considered as acceptable, the adoption of Wi-Fi-based localization for (a highly mobile) unmanned aerial vehicle (UAV) has received limited attention. In this article, a low-cost and low-complexity system architecture is proposed and exploited to perform a comparative analysis between two Wi-Fi-based localization approaches: the traditional received signal strength indicator (RSSI) ranging and the more recent fine time measurement (FTM), based on the IEEE 802.11mc amendment. Our goal is to estimate and compare the efficacy of the proposed system for real-time positioning of a static or moving UAV, evaluating the impact of different filtering solutions on the localization accuracy. The obtained results show that FTM-based localization is more accurate, reducing the positioning error by 37% with respect to the RSSI-based positioning approach. Our results also confirm the better overall performance of the FTM-based solution for low-cost localization applications, discussing its limitations, scalability, and advantages as a viable backup positioning solution in (weak or denied) Global Navigation Satellite System-based environments and scenarios.},

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@article{padacipafe:2024:iop,

title = {On UAV Terrestrial Connectivity Enhancement through Smart Selective Antennas},

author = {Emanuele Pagliari and Luca Davoli and Giordano Cicioni and Valentina Palazzi and Gianluigi Ferrari},

doi = {10.1088/1742-6596/2716/1/012057},

year  = {2024},

date = {2024-03-17},

urldate = {2024-03-17},

journal = {Journal of Physics: Conference Series},

volume = {2716},

number = {1},

pages = {012057},

publisher = {IOP Publishing},

abstract = {Nowadays, Unmanned Aerial Vehicles (UAVs) are widely used in heterogeneous contexts and, thanks to a continuous technological evolution, are going to be used for several applications such as, for example, Beyond Visual Line of Sight (BVLOS) operations. Since in BVLOS flights the UAV and the ground control center may not have a direct visibility with each other, a robust communication system is needed to provide reliable connectivity. Although a cellular (4G/5G) network is the current best candidate to enable BVLOS applications, there are still some limitations to overcome, as 4G (LTE) and 5G (NR) cellular networks are natively designed for terrestrial use. In this paper, we first investigate current cellular communication limitations for UAV-based applications, in particular taking into account both results available in the literature, as well as experimental performance campaigns. Then, a viable solution for mitigating these drawbacks exploiting selective on-board antennas is proposed, whose performance is experimentally investigated with a preliminary prototypical architecture.},

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@article{modadhkufera:24:jnca,

title = {Throughput and delay analysis of cognitive M2M communications},

author = {Soumen Mondal and Luca Davoli and Sanjay Dhar Roy and Sumit Kundu and Gianluigi Ferrari and Riccardo Raheli},

doi = {10.1016/j.jnca.2024.103856},

issn = {1084-8045},

year  = {2024},

date = {2024-03-04},

urldate = {2024-01-01},

journal = {Journal of Network and Computer Applications},

volume = {225},

pages = {103856},

abstract = {In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.},

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}

@article{cidafe:2024:tmc,

title = {LoRa Meets IP: A Container-Based Architecture to Virtualize LoRaWAN End Nodes},

author = {Antonio Cilfone and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/TMC.2024.3359150},

issn = {1558-0660},

year  = {2024},

date = {2024-01-26},

urldate = {2024-10-01},

journal = {IEEE Transactions on Mobile Computing},

volume = {23},

number = {10},

pages = {9191-9207},

abstract = {In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes’ side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.},

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@article{padacipamealrofe:2023:access,

title = {Smart Selective Antennas System (SSAS): Improving 4G LTE Connectivity for UAVs Using Directive Selective Antennas},

author = {Emanuele Pagliari and Luca Davoli and Giordano Cicioni and Valentina Palazzi and Paolo Mezzanotte and Federico Alimenti and Luca Roselli and Gianluigi Ferrari},

doi = {10.1109/ACCESS.2023.3347335},

issn = {2169-3536},

year  = {2023},

date = {2023-12-25},

urldate = {2024-01-01},

journal = {IEEE Access},

volume = {12},

pages = {7040-7062},

abstract = {In this paper, the prototypical deployment of a Multiple-Input-Multiple-Output (MIMO) antennas system, denoted as Smart Selective Antennas System (SSAS), aiming at mitigating inter-cell interference effects of cellular networks for in-flight Unmanned Aerial Vehicles (UAVs), is discussed. In detail, the proposed SSAS is beneficial to increase the communication reliability over existing cellular networks, especially with regard to complex Beyond Visual Line of Sight (BVLOS) drones’ missions and applications. Its deployment is motivated as existing 4G Long Term Evolution (LTE) cellular networks (as well as 5G networks) are mainly designed and optimized for terrestrial utilization, thus not taking into account interference effects on flying connected devices. The prototypical implementation of the SSAS has been expedient to conduct multiple experimental flights with a drone at different altitudes, collecting performance results and validating the proposed SSAS as a viable solution for inter-cell interference mitigation.},

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}

@article{dabefe:2023:dib,

title = {Air quality dataset from an indoor airport travelers transit area},

author = {Luca Davoli and Laura Belli and Gianluigi Ferrari},

doi = {10.1016/j.dib.2023.109821},

issn = {2352-3409},

year  = {2023},

date = {2023-11-20},

urldate = {2024-01-01},

journal = {Data in Brief},

volume = {52},

pages = {109821},

abstract = {The experimental dataset (organized in a semicolon-separated text format) is composed by air quality records collected over a 1-year period (October 2022-October 2023) in an indoor travelers’ transit area in the Brindisi airport, Italy. In detail, the dataset consists of three CSV files (ranging from 7M records to 11M records) resulting from the on-field data collection performed by three prototypical Internet of Things (IoT) sensing nodes, designed and implemented at the IoTLab of the University of Parma, Italy, featuring a Raspberry Pi 4 (as processing unit) which three low-cost commercial sensors (namely: Adafruit MiCS5524, Sensirion SCD30, Sensirion SPS30) are connected to. The sensors sample the air in the monitored static indoor environment every 2 s. Each collected record composing the experimental dataset contains (i) the identifier of the IoT node that sampled the air parameters; (ii) the presence of gases (as a unified value concentration); (iii) the concentration of carbon dioxide (CO2) in the travelers’ transit area, together with air temperature and humidity; and (iv) the concentration of particulate matter (PM) in the indoor monitored environment – in terms of particles’ mass concentration (µg/m3), number of particles (#/cm3), and typical particle size (µm) – for particles with a diameter up to 0.5 µm (PM0.5), 1 µm (PM1), 2.5 µm (PM2.5), 4 µm (PM4), and 10 µm (PM10). Therefore, on the basis of the monitored air parameters in the indoor travelers’ transit area, the experimental dataset might be expedient for further analyses – e.g., for calculating Air Quality Indexes (AQIs) taking into account the collected information – and for comparison with information sampled in different contexts and scenarios – examples could be indoor domestic environments, as well as outdoor monitoring in smart cities or public transports.},

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}

@article{padafe:2023:iotj,

title = {Harnessing Communication Heterogeneity: Architectural Design, Analytical Modeling, and Performance Evaluation of an IoT Multi-Interface Gateway},

author = {Emanuele Pagliari and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/JIOT.2023.3317672},

year  = {2023},

date = {2023-09-20},

urldate = {2024-01-01},

journal = {IEEE Internet of Things Journal},

volume = {11},

number = {5},

pages = {8030-8051},

abstract = {Given the massive deployment of Internet of Things (IoT) applications over the last decade, the need for gateways able to efficiently route information flows across multiple heterogeneous networks has emerged, bringing new challenges. Therefore, the design and implementation of IoT gateways is crucial. In this article, with reference to the architecture of a prototypical multi-interface gateway (MIG) (based on commercial-off-the-shelf (COTS) devices), we evaluate its performance: 1) analytically, through an innovative Markov chain-based model; 2) by simulation, with a Python simulator; and 3) experimentally, through the (starting) COTS device-based prototype. In detail, the MIG is equipped with heterogeneous wireless communication interfaces (namely, LoRaWAN, BLE, cellular 4G Cat. 4, and IEEE 802.11 Wi-Fi 2.4 GHz) and is applicable to multiple IoT scenarios. The obtained simulation and experimental results show the validity of the proposed analytical model. Further improvements of the proposed framework are eventually discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bedafe:2023:computer,

title = {Smart City as an Urban Intelligent Digital System: The Case of Parma},

author = {Laura Belli and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/MC.2023.3267245},

issn = {1558-0814},

year  = {2023},

date = {2023-06-16},

urldate = {2023-07-01},

journal = {Computer},

volume = {56},

number = {7},

pages = {106-109},

abstract = {A smart city is an intelligent digital system that implements an effective smart urban environment able to integrate information from heterogeneous data sources and to provide efficient high-level services to citizens and municipal authorities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{camadaciyuwafe:2023:comnet,

title = {Experimental analysis of RSSI-based localization algorithms with NLOS pre-mitigation for IoT applications},

author = {Fabrizio Carpi and Marco Martalò and Luca Davoli and Antonio Cilfone and Yingjie Yu and Yi Wang and Gianluigi Ferrari},

doi = {10.1016/j.comnet.2023.109663},

issn = {1389-1286},

year  = {2023},

date = {2023-03-06},

urldate = {2023-01-01},

journal = {Computer Networks},

volume = {225},

pages = {109663},

abstract = {In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).},

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pubstate = {published},

tppubtype = {article}

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@article{hidagrfe:2023,

title = {DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks},

author = {Christian Hirsch and Luca Davoli and Radu Grosu and Gianluigi Ferrari},

doi = {10.1016/j.comnet.2023.109560},

issn = {1389-1286},

year  = {2023},

date = {2023-01-10},

urldate = {2023-01-01},

journal = {Computer Networks},

volume = {222},

pages = {109560},

abstract = {Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37µA per data transmission and around 41.37µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.},

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tppubtype = {article}

}

@article{dabedetaabfe:2022:cisap10,

title = {On Safety Enhancement in IIoT Scenarios Through Heterogeneous Localization Techniques},

author = {Luca Davoli and Laura Belli and Francesco Denaro and Dinesh Tamang and Andrea Abrardo and Gianluigi Ferrari},

doi = {10.3303/CET2291044},

year  = {2022},

date = {2022-06-15},

urldate = {2022-06-01},

journal = {Chemical Engineering Transactions},

volume = {91},

pages = {259-264},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dapafe:2021:drones,

title = {Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming},

author = {Luca Davoli and Emanuele Pagliari and Gianluigi Ferrari},

doi = {10.3390/drones5020026},

issn = {2504-446X},

year  = {2021},

date = {2021-06-24},

urldate = {2021-06-24},

journal = {Drones},

volume = {5},

number = {2},

abstract = {Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases.},

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pubstate = {published},

tppubtype = {article}

}

@article{codafe:2021:sensors,

title = {Forecasting Air Temperature on Edge Devices with Embedded AI},

author = {Gaia Codeluppi and Luca Davoli and Gianluigi Ferrari},

doi = {10.3390/s21123973},

issn = {1424-8220},

year  = {2021},

date = {2021-06-09},

journal = {Sensors},

volume = {21},

number = {12},

pages = {1-29},

abstract = {With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402°C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible.},

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tppubtype = {article}

}

@article{dapacabaviabfe,

title = {Ultrasonic-Based Environmental Perception for Mobile 5G-Oriented XR Applications},

author = { Luca Davoli and Ioannis Paraskevopoulos and Cinzia Campanella and Stefano Bauro and Tommaso Vio and Andrea Abrardo and Gianluigi Ferrari},

doi = {10.3390/s21041329},

issn = {1424-8220},

year  = {2021},

date = {2021-02-13},

journal = {Sensors},

volume = {21},

number = {4},

pages = {1329},

publisher = {MDPI AG},

abstract = {One of the sectors that is expected to significantly benefit from 5G network deployment is eXtended Reality (XR). Besides the very high bandwidth, reliability, and Quality of Service (QoS) to be delivered to end users, XR also requires accurate environmental perception for safety reasons: this is fundamental when a user, wearing XR equipment, is immersed in a “virtual” world, but moves in a “real” environment. To overcome this limitation (especially when using low-cost XR equipments, such as cardboards worn by the end user), it is possible to exploit the potentialities offered by Internet of Things (IoT) nodes with sensing/actuating capabilities. In this paper, we rely on ultrasonic sensor-based IoT systems to perceive the surrounding environment and to provide “side information” to XR systems, then performing a preliminary experimental characterization campaign with different ultrasonic IoT system configurations worn by the end user. The combination of the information flows associated with XR and IoT components is enabled by 5G technology. An illustrative experimental scenario, relative to a “Tourism 4.0” IoT-aided VR application deployed by Vodafone in Milan, Italy, is presented.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Davoli_2020,

title = {On Driver Behavior Recognition for Increased Safety: A Roadmap},

author = {Luca Davoli and Marco Martalò and Antonio Cilfone and Laura Belli and Gianluigi Ferrari and Roberta Presta and Roberto Montanari and Maura Mengoni and Luca Giraldi and Elvio G. Amparore and Marco Botta and Idilio Drago and Giuseppe Carbonara and Andrea Castellano and Johan Plomp},

doi = {10.3390/safety6040055},

issn = {2313-576X},

year  = {2020},

date = {2020-12-24},

journal = {Safety},

volume = {6},

number = {4},

pages = {1-33},

publisher = {MDPI AG},

abstract = {Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.},

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}