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Smartphone Sensors for Driver Behavior Monitoring Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (10 March 2023) | Viewed by 43398

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Special Issue Editors

Dr. Alexey Kashevnik

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Guest Editor

St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg 199178, Russia
Interests: intelligent transportation systems; driver monitoring systems; artificial intelligence; knowledge-based systems; smart spaces; Internet of Things; cloud computing; ontologies; context management; knowledge management; user profiling; robotics; recommendation systems; decision support systems; socio–cyberphysical systems

Prof. Dr. Andrei Gurtov

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Guest Editor

Department of Computer and Information Science, Linköping University, SE-581 83 Linköping, Sweden
Interests: driver monitoring systems; network security; industrial Internet; cyber physical systems; resource allocation in cloud computing; mobile; wireless; sensor networks; applying game theory to computer networks and systems; host identity protocol; 5G; cognitive networks; multipath congestion control; peer-to-peer communication

Dr. Sara Ferreira

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Guest Editor

Research Centre for Territory, Transports and Environment (CITTA), Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
Interests: accident modeling; road safety; road users’ behavior; technological sciences; engineering; civil engineering; infrastructures engineering
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Special Issue Information

Driver monitoring is a vital topic nowadays due to the high number of traffic accidents on public roads around the world. Because self-driving cars are not yet available everywhere, research and development in the field of intelligent transportation systems concentrates on driver monitoring. This Special Issue covers smartphone sensor utilization for the purpose of driver monitoring, as well as security aspects for when a driver tries to fool the app while in a dangerous state. The smartphone’s front-facing camera tracks the driver’s face and computer vision algorithms are used to detect dangerous situations. In addition to the front-facing camera, the rear camera detects dangerous situations on the road. Dangerous situations are defined by the driver’s behavior. Other sensors, such as GPS, accelerometer, gyroscope, and magnetometer, are used for context situation detection interpretation. This Special Issue also covers mobile application integration with vehicle infotainment systems via Android Auto and Apple CarPlay. Both review articles and original research papers are welcome. There is particular interest in papers concerning new applications and innovative approaches to driver monitoring.

Dr. Alexey Kashevnik
Prof. Andrei Gurtov
Prof. Sara Ferreira
Guest Editors

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Keywords

driver monitoring
intelligent transportation systems
smartphone
smartphone sensors
computer vision
vehicle infotainment systems
security

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Published Papers (9 papers)

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Research

14 pages, 16189 KiB

Open AccessArticle

3D Vehicle Detection and Segmentation Based on EfficientNetB3 and CenterNet Residual Blocks

by Alexey Kashevnik and Ammar Ali

Sensors 2022, 22(20), 7990; https://doi.org/10.3390/s22207990 - 20 Oct 2022

Cited by 9 | Viewed by 3741

Abstract

In this paper, we present a two stages solution to 3D vehicle detection and segmentation. The first stage depends on the combination of EfficientNetB3 architecture with multiparallel residual blocks (inspired by CenterNet architecture) for 3D localization and poses estimation for vehicles on the scene. The second stage takes the output of the first stage as input (cropped car images) to train EfficientNet B3 for the image recognition task. Using predefined 3D Models, we substitute each vehicle on the scene with its match using the rotation matrix and translation vector from the first stage to get the 3D detection bounding boxes and segmentation masks. We trained our models on an open-source dataset (ApolloCar3D). Our method outperforms all published solutions in terms of 6 degrees of freedom error (6 DoF err). Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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34 pages, 899 KiB

Open AccessArticle

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer’s Point of View

by Dmitry Levshun, Andrey Chechulin and Igor Kotenko

Sensors 2022, 22(13), 5063; https://doi.org/10.3390/s22135063 - 5 Jul 2022

Cited by 1 | Viewed by 4556

Abstract

Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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21 pages, 6295 KiB

Open AccessArticle

An Approach to the Automatic Construction of a Road Accident Scheme Using UAV and Deep Learning Methods

by Anton Saveliev, Valeriia Lebedeva, Igor Lebedev and Mikhail Uzdiaev

Sensors 2022, 22(13), 4728; https://doi.org/10.3390/s22134728 - 23 Jun 2022

Cited by 10 | Viewed by 2623

Abstract

Recreating a road traffic accident scheme is a task of current importance. There are several main problems when drawing up a plan of accident: a long-term collection of all information about an accident, inaccuracies, and errors during manual data fixation. All these disadvantages affect further decision-making during a detailed analysis of an accident. The purpose of this work is to automate the entire process of operational reconstruction of an accident site to ensure high accuracy of measuring the distances of the relative location of objects on the sites. First the operator marks the area of a road accident and the UAV scans and collects data on this area. We constructed a three-dimensional scene of an accident. Then, on the three-dimensional scene, objects of interest are segmented using a deep learning model SWideRNet with Axial Attention. Based on the marked-up data and image Transformation method, a two-dimensional road accident scheme is constructed. The scheme contains the relative location of segmented objects between which the distance is calculated. We used the Intersection over Union (IoU) metric to assess the accuracy of the segmentation of the reconstructed objects. We used the Mean Absolute Error to evaluate the accuracy of automatic distance measurement. The obtained distance error values are small (0.142 ± 0.023 m), with relatively high results for the reconstructed objects’ segmentation (IoU = 0.771 in average). Therefore, it makes it possible to judge the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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27 pages, 4040 KiB

Open AccessArticle

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition

by Evgenia Novikova, Dmitry Fomichov, Ivan Kholod and Evgeny Filippov

Sensors 2022, 22(8), 2983; https://doi.org/10.3390/s22082983 - 13 Apr 2022

Cited by 14 | Viewed by 3466

Abstract

Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security and privacy. The federated learning (FL) computation paradigm has been proposed as a privacy-preserving computational model that allows securing the privacy of the data owner. However, it still has no formal proof of privacy guarantees, and recent research showed that the attacks targeted both the model integrity and privacy of the data owners could be performed at all stages of the FL process. This paper focuses on the analysis of the privacy-preserving techniques adopted for FL and presents a comparative review and analysis of their implementations in the open-source FL frameworks. The authors evaluated their impact on the overall training process in terms of global model accuracy, training time and network traffic generated during the training process in order to assess their applicability to driver’s state and behaviour monitoring. As the usage scenario, the authors considered the case of the driver’s activity monitoring using the data from smartphone sensors. The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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12 pages, 2131 KiB

Open AccessArticle

Threats Detection during Human-Computer Interaction in Driver Monitoring Systems

by Alexey Kashevnik, Andrew Ponomarev, Nikolay Shilov and Andrey Chechulin

Sensors 2022, 22(6), 2380; https://doi.org/10.3390/s22062380 - 19 Mar 2022

Cited by 4 | Viewed by 2751

Abstract

This paper presents an approach and a case study for threat detection during human–computer interaction, using the example of driver–vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human–system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver–ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator–computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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15 pages, 2198 KiB

Open AccessArticle

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

by Jose M. Celaya-Padilla, Jonathan S. Romero-González, Carlos E. Galvan-Tejada, Jorge I. Galvan-Tejada, Huizilopoztli Luna-García, Jose G. Arceo-Olague, Nadia K. Gamboa-Rosales, Claudia Sifuentes-Gallardo, Antonio Martinez-Torteya, José I. De la Rosa and Hamurabi Gamboa-Rosales

Sensors 2021, 21(22), 7752; https://doi.org/10.3390/s21227752 - 21 Nov 2021

Cited by 22 | Viewed by 12648

Abstract

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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15 pages, 1209 KiB

Open AccessArticle

In-Vehicle Situation Monitoring for Potential Threats Detection Based on Smartphone Sensors

by Alexey Kashevnik, Andrew Ponomarev, Nikolay Shilov and Andrey Chechulin

Sensors 2020, 20(18), 5049; https://doi.org/10.3390/s20185049 - 5 Sep 2020

Cited by 9 | Viewed by 3453

Abstract

This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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22 pages, 9024 KiB

Open AccessArticle

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

by Hailun Zhang and Rui Fu

Sensors 2020, 20(17), 4887; https://doi.org/10.3390/s20174887 - 28 Aug 2020

Cited by 30 | Viewed by 4461

Abstract

At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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15 pages, 303 KiB

Open AccessArticle

Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research

by António Lobo, Sara Ferreira and António Couto

Sensors 2020, 20(14), 3836; https://doi.org/10.3390/s20143836 - 9 Jul 2020

Cited by 14 | Viewed by 3419

Abstract

Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment. Full article

(This article belongs to the Special Issue Smartphone Sensors for Driver Behavior Monitoring Systems)

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