Intelligent Human-machine Interaction Research Articles

Bioinspired breathable biodegradable bioelastomer-based flexible wearable electronics for high-sensitivity human-interactive sensing

Flexible wearable electronic sensors are regarded to facilitate revolutionary changes in human's daily life for their great promise in multifunctional artificial electronic skins, personalized healthcare monitoring, and smart human–machine interaction. However, there remains great challenges to simultaneously achieve high sensitivity, broad sensing range and robust cyclic stability for high-performance personal diagnostic healthcare monitoring, excellent breathability for comfortable wearing, as well as environmentally friendly biodegradability for reducing electronic wastes. Herein, bioinspired by three-dimensional network structure of ant nest with high specific surface area, interconnected channels, and robust ventilation, a wearable, breathable, and biodegradable electronic sensor is prepared from facile face-to-face assembly of breathable biodegradable MXene-coated poly(1,8-octanediol-co-polycaprolactone citrate) elastomer sponge (MXene@POPLC), and an MXene ink-printed interdigitated electrode-coated breathable electrospun film. The as-obtained flexible electronic sensor exhibits high sensitivity (up to 246.3 kPa−1), broad sensing range (up to 333.3 kPa), reliable cycling stability (∼20,000 cycles), fast response (10 ms)/recovery time, robust breathability, excellent biocompatibility, facile biodegradability, and wireless wearable human–machine interfacing. Therefore, this line of research work opens up a novel avenue for the preparation of multifunctional wearable, breathable, and biodegradable electronics to hold great promise in smart healthcare monitoring, intelligent human–machine interaction, smart personal protection and next-generation artificial skin.

Ultrafast readout, crosstalk suppression iontronic array enabled by frequency-coding architecture

The development of iontronic skin (I-skin) capable of ultrafast sensing in a wide pressure range, comparable to human skin, is of paramount importance for intelligent robotics. However, this remains a major challenge due to the lack of iontronic array architectures that can achieve ultrafast readout and crosstalk-free under large capacitance response generated within a wide pressure range. Here, we report a frequency-coding architecture of artificial ion mechanoreceptor skin (AIM-skin) that can provide a universal mode of iontronic array sensing and bypass the dependence of complex integrated back-end interface electronics. Notably, the successful implementation of orthogonal frequency coding in the AIM-skin with high sensitivity and ultrawide pressure range achieve ultrafast parallel readout for the spatiotemporal mechanical stimuli. Furthermore, the parallel zero-potential mechanism (PZPM) of the architecture effectively mitigates electrical crosstalk between sensing units. We have demonstrated that combhination of proposed device and deep learning has a broad application prospect in intelligent human-machine interaction and real-time dynamic robotic manipulation.

An Intelligent Glove System for Real‐Time Multiple Electrical Signal Acquisition and Display

AbstractRealizing multimodal tactile perception for robots is highly desirable for achieving more intelligent human–machine interaction. A real‐time intelligent glove system that can capture and monitor hand tactile information, including pressure, bending, and temperature is proposed. This system processes the collected data and visualizes the tactile information in a 2D image. The intelligent glove system integrates a palm‐shaped sensor unit, a multi‐channel data acquisition board, and a data analysis module. The glove consists of 34 flexible sensors, including 28 pressure sensors, five bending sensors, and one temperature sensor, which convert biological information into electrical signals. By enhancing the performance of the system, the intelligent glove achieves high sensitivity and good fault tolerance, allowing it to analyze the multimodal tactile information of the entire hand with fast responsivity. By analyzing the acquired multi‐channel electrical signals, the system can analyze object shapes and various gripping states. This work paves the way for wearable devices used in multimodal tactile perception and promotes the emergence of new modes of human–machine interaction.

Flexible Bioinspired Healable Antibacterial Electronics for Intelligent Human-Machine Interaction Sensing.

Flexible electronic sensors are receiving numerous research interests for their potential in electronic skins (e-skins), wearable human-machine interfacing, and smart diagnostic healthcare sensing. However, the preparation of multifunctional flexible electronics with high sensitivity, broad sensing range, fast response, efficient healability, and reliable antibacterial capability is still a substantial challenge. Herein, bioinspired by the highly sensitive human skin microstructure (protective epidermis/spinous sensing structure/nerve conduction network), a skin bionic multifunctional electronics is prepared by face-to-face assembly of a newly prepared healable, recyclable, and antibacterial polyurethane elastomer matrix with conductive MXene nanosheets-coated microdome array after ingenious templating method as protective epidermis layer/sensing layer, and an interdigitated electrode as signal transmission layer. The polyurethane elastomer matrix functionalized with triple dynamic bonds (reversible hydrogen bonds, oxime carbamate bonds, and copper (II) ion coordination bonds) is newly prepared, demonstrating excellent healability with highly healing efficiency, robust recyclability, and reliable antibacterial capability, as well as good biocompatibility. Benefiting from the superior mechanical performance of the polyurethane elastomer matrix and the unique skin bionic microstructure of the sensor, the as-assembled flexible electronics exhibit admirable sensing performances featuring ultrahigh sensitivity (up to 1573.05kPa-1 ), broad sensing range (up to 325kPa), good reproducibility, the fast response time (≈4ms), and low detection limit (≈0.98Pa) in diagnostic human healthcare monitoring, excellent healability, and reliable antibacterial performance.

A biomimetic flexible sensor inspired by Albuca namaquensis for simultaneous high stretchability and strength

In the realm of intelligent human-machine interaction, the expansive potential of robust flexible wearable electronic devices emerges prominently. However, the practicality of such flexible electronic devices is often constrained within scenarios involving substantial mechanical strains, such as high-intensity (>100 MPa) and high tensile (>1000%) deformation, particularly in articulation-rich joint regions. Specifically, the substrates of flexible sensors confront challenges in reconciling elongation rates and tensile strengths, for which a cogent strategy is currently lacking to address the aforementioned mechanical parameter requisites. To address this quandary, the present study employs a paradigm of microscale materials that synergistically integrate rigid-flexible coupling enhancements, coupled with a biomimetic progressively asymptotic stretch optimization approach at the macroscale structural level. Through an inside-out methodology, we have ingeniously devised a biomimetic flexible sensory electronic device that seamlessly integrates sensing media and load-bearing capabilities, uniquely poised for simultaneous deployment within high-intensity and high-stretchability scenarios. By employing a judicious combination of rigidly supporting styrene monomers and the polymerized three-dimensional scaffold, formed through the coupling of elastomeric sustaining ethylene-butadiene monomers, with the high-strength, highly conductive discrete medium of graphene, we have achieved the successful synthesis of a substrate boasting a mechanical strength of 121.4 MPa alongside an impressive 1109% elongation capacity. This substrate exhibits a remarkable electrical conductivity up to 106 S*m−1. Furthermore, drawing inspiration from the serpentine architecture of the Albuca namaquensis, a two-tiered gradient stretching approach has been harnessed, resulting in an approximate 2.8-fold enhancement in stretchability. Leveraging the ingeniously designed GSBS sensor, an online testing platform for human-machine interaction capable of accommodating substantial deformations at joint interfaces is established. Through this innovation, we have effectively orchestrated the synchronization and emulation of extensive tensile movements between mechanical prosthetics and human articulations.

Flexible Optoelectronic Multimodal Proximity/Pressure/Temperature Sensors with Low Signal Interference.

Multimodal tactile sensors are a crucial part of intelligent human-machine interaction and collaboration. Simultaneous detection of proximity, pressure, and temperature on a single sensor can greatly promote the safety, interactivity and compactness of interaction systems. However, severe signal interference and complex decoupling algorithm hinder the actual applications. Here, we report a flexible optoelectronic multimodal sensor capable of detecting and decoupling proximity/pressure/temperature by integrating a light waveguide and an interdigital electrode into a compact fibrous sensor. Negligible signal interference is realized by combining heterogeneous sensing mechanisms of optics and electronics, which encodes proximity into capacitance, pressure into light intensity and temperature into resistance. The sensor exhibited a large sensing distance of 225 mm with fast responses for proximity detection, a pressure sensitivity of 0.42 N-1 , and a temperature sensitivity of 7%/°C. As a proof of concept, the doll equipped with the sensor can accurately discriminate and detect various stimuli, thus achieving safe and immersive interactions with the user. This work opens up promising paths for self-decoupled multimodal sensors and related human/machine/environment interaction applications. This article is protected by copyright. All rights reserved.

A Skin‐Bioinspired Urchin‐Like Microstructure‐Contained Photothermal‐Therapy Flexible Electronics for Ultrasensitive Human‐Interactive Sensing

AbstractWearable electronic sensors have attracted extensive attention in multifunctional electronic skin, personalized health monitoring, intelligent human–machine interaction, and smart medical treatment. However, critical challenge exists in simultaneously achieving excellent sensing performances with high sensitivity, rapid response, low sensing limit, and excellent cycling stability for full‐scale human healthcare detection and further timely photothermal therapy. For highly sensitive human skin, the spinosum microstructure in epidermis and dermis takes an important part in sensing signal amplification and transmission. Inspired by the spinosum microstructure of human skin for highly sensitive tactile perception, a skin‐inspired flexible electronic sensor is prepared from the face‐to‐face assembly of an as‐prepared polybutylene adipate‐polyurethane (PBAPU) elastomer matrix with conducting MXene nanosheets‐coated urchin‐like microstructure templated from natural chrysanthemum pollen grain microstructures, and an interdigitated electrode‐coated PBAPU elastomer substrate. The PBAPU elastomer matrix is newly prepared, exhibiting outstanding tensile strength (18.87 MPa), high stretchability (1190%), and comparable elastic modulus (1.7 MPa) to human skin. The as‐assembled flexible electronic sensor exhibits a highly sensitive sensitivity (up to 784.02 kPa−1), low detection limit (0.12 Pa), and reliable cycling stability for intelligent human–machine interfacing. The MXene nanosheets‐coated urchin‐like microstructure‐contained PBAPU possesses efficient photothermal heating performance to achieve on‐demand photothermal therapy for rehabilitation training.

Finger–coding intelligent human–machine interaction system based on all–fabric ionic capacitive pressure sensors

The comprehensive application of human–machine interaction (HMI) has promoted humanization in digital life. Wearable electronic devices and artificial intelligence are driving the development and transformation of HMI technology. Nevertheless, the fabrication of superior devices assisted by artificial intelligence for HMI remains challenging. We introduce an all–fabric ionic capacitive pressure sensor (AFICPS) using laser–induced graphene/fabric as the electrode and ion/fabric as the dielectric layer. The designed sensor exhibits high performance, including ultrahigh sensitivity (17.8 kPa−1@0–60 kPa, 73.3 kPa−1@60–100 kPa, and 33.8 kPa−1@100–150 kPa), wide detection range (0–150 kPa), ultrafast response and recovery times (11 and 8 ms, respectively), low detection limit (∼ 5 Pa), good mechanical stability (only 4.4 % performance loss after 5000 cycles), and excellent breathability and biocompatibility (mouse fibroblast cell survival exceeding 99 % after 48 h). All these factors contribute to the accurate detection of human physiological signals, particularly finger movements. Hence, we demonstrate a finger–coding intelligent HMI system that converts multiple gestures into 26 English letters and 10 Arabic numerals with a recognition accuracy above 98 % by using machine learning. This type of system may enhance information privacy and be of great significance in HMI and encrypted communication.

Anisotropic aerogel induced gelatin hydrogel actuator with favourable mechanical properties and multiple solvent responsiveness

As an important part of biomimetic/intelligent new materials, the hydrogel actuator can sense changes in the external environment and convert external energy into mechanical energy, therefore attracting extensive attention in many fields. However, the relative weak mechanical properties, small driving force and single responsiveness to external stimuli of hydrogel-based actuators are still the main factor restricting their practical application and future development. In this paper, a heterogeneous hydrogel-based actuator with favourable mechanical properties and multiple solvent responsiveness is constructed via the synergistic action of anisotropic chitosan aerogel as the skeleton and solvent-sensitive gelatin hydrogel as muscle in the actuator framework. The addition of aerogel skeleton and the salting out of gelatin favor the actuator favourable mechanical behavior with maximum tensile stress and compressive stress up to MPa level. Furthermore, the heterogeneous aerogel/hydrogel composite structure greatly improves the driving capability of actuator, presenting multiple solvent responsiveness with the maximum driving amplitude up to 730° in the ion solution. The well-designed actuators can be exploried as intelligen microfluidic valves that recognize and pass specific solutions at different flow rates. This aerogel/hydrogel composite actuator has broad application prospects in microfluidic systems, smart valves, artificial muscles and intelligent human-machine interaction.

A Framework for Service-Oriented Digital Twin Systems for Discrete Workshops and Its Practical Case Study

To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the industrial IoT platform as the system-service platform to solve the problems of the opaque monitoring of operators in discrete workshops, the low interactivity of 2D monitoring systems, and the difficulty of the visual monitoring of workshop data. Firstly, the current situation of intelligent manufacturing workshop-monitoring demand in the context of new-generation information technology is analyzed, and a six-dimensional digital-twin-workshop-monitoring architecture is proposed, whereby a discrete workshop monitoring system based on the digital twin is constructed with IoT as the service platform. We will conduct research on the construction of virtual workshops for the system development process, twin data collection based on edge computing gateways, and dynamic monitoring of the production process. Finally, through the application of this system framework in a movable-arm-production workshop, the more intelligent human–machine interaction process of browsing and controlling workshop information, such as the equipment layout and production processes in the virtual workshop, has been realized. This includes data acquisition based on edge-computing gateways, dynamic real-time monitoring of the production process, etc., which provides a reference for realizing the visual monitoring of the discrete workshop.

Recent Progress of Energy-Storage-Device-Integrated Sensing Systems

With the rapid prosperity of the Internet of things, intelligent human-machine interaction and health monitoring are becoming the focus of attention. Wireless sensing systems, especially self-powered sensing systems that can work continuously and sustainably for a long time without an external power supply have been successfully explored and developed. Yet, the system integrated by energy-harvester needs to be exposed to a specific energy source to drive the work, which provides limited application scenarios, low stability, and poor continuity. Integrating the energy storage unit and sensing unit into a single system may provide efficient ways to solve these above problems, promoting potential applications in portable and wearable electronics. In this review, we focus on recent advances in energy-storage-device-integrated sensing systems for wearable electronics, including tactile sensors, temperature sensors, chemical and biological sensors, and multifunctional sensing systems, because of their universal utilization in the next generation of smart personal electronics. Finally, the future perspectives of energy-storage-device-integrated sensing systems are discussed.

Flexible breathable photothermal-therapy epidermic sensor with MXene for ultrasensitive wearable human-machine interaction

Multifunctional flexible wearable epidermic sensors have gained enormous attention for their promising potential applications in personal health management, electronic skins and intelligent human-machine interaction. However, it remains a critical challenge to realize a flexible breathable sensing device with reliable sensing performances for comfortably wearable human motion detection and ultrasensitive human-machine interfacing with the integrated functional capability of disease diagnosis and subsequent photothermal therapy simultaneously. Herein, a flexible breathable epidermic sensor is fabricated by delicately assembling conductive MXene nanosheets and silver nanowires (AgNWs) onto electrospun elastomeric substrates for ultrasensitive sensing and on-demand photothermal therapy. The as-prepared flexible breathable electronic sensor presents a broad sensing range (up to ∼120 %), ultrahigh sensitivity (gauge factor (GF) up to 4720), extremely low detection limit, remarkable reliability, and robust biocompatibility for serving as the electronic skin to monitor human physiological signals and wearable human-machine interaction. Moreover, it exhibits brilliant photothermal properties for enhanced antibacterial effect and timely photothermal therapy after healthcare monitoring and disease diagnosis. This work provides a facile way of developing flexible breathable multifunctional electronic sensors featured with excellent sensing ability, brilliant breathability and reliable photothermal properties for long-term healthcare monitoring, ultrasensitive wearable human-machine interfacing, smart disease diagnosis and treatment.

Flexible Optical Fiber-Based Smart Textile Sensor for Human–Machine Interaction

Flexible sensors are the main components of wearable devices. Compared with flexible substrate-based skin-like sensors, flexible textile sensors have the intrinsic advantages of excellent breathability, compliance with the human body and comfort to human skin. In this article, a flexible optical fiber-based smart textile (FOFT) sensor has been proposed for the application of intelligent human–machine interaction (HMI) and healthcare. The FOFT sensor integrated fiber Bragg gratings (FBGs) with yarns in the way of the plain weave. A double-layer flexible optical fiber crossover structure is proposed to improve the measurement accuracy of the sensor for human body signals. Such a structure leads to a highly flexible textile and wearing comfort. The weak characteristic information of the human body can be extracted by the sensor based on the wavelength demodulation of double FBGs. Combined with the backpropagation (BP) neural network, an FOFT sensor-based gesture recognition application with an off-line accuracy of 97.02% has been developed. Furthermore, the FOFT sensor is used to monitor the human respiratory signals under different postures and obtain the respiratory rate. A series of experiments demonstrate that the FOFT sensor has a huge potential in HMI and healthcare.

Porous-Structure-Promoted Tribo-Induced High-Performance Self-Powered Tactile Sensor toward Remote Human-Machine Interaction.

Self-powered tactile sensor with versatile functions plays a significant role in the development of an intelligent human-machine interaction (HMI) system. Herein, a hybrid self-powered porous-structured tactile sensor (SPTS) is proposed by monolithically integrating a porous triboelectrification-induced electroluminescence (TIEL) component and a single-electrode triboelectric nanogenerator with the high charge generation in the bulk volume. At a low pressure of 10kPa, TIEL intensity can be significantly improved by three times, which is superior to that in previous reports, with enhanced triboelectricity. Based on the enhancement brought by the porous structure and optimized parameters, the SPTS achieves significant sensing performance in both optical and electrical modes. To demonstrate the potential of practical applications, a programmable optical and electrical dual-mode HMI system is established based on SPTS to remotely control an intelligent vehicle and operate a computer game through identifying finger touch trajectories. This work not only contributes a new economical-effective methodology toward a high-performance tribo-induced self-powered tactile sensor but also facilitates the remote control of HMI with dual-mode functionality, which has broad potential applications in the fields of intelligent robots, augmented reality, flexible wearable electronics, and smart home.

Highly Conformal Polymers for Ambulatory Electrophysiological Sensing.

Stable ambulatory electrophysiological sensing is widely used for smart e-healthcare monitoring, clinical diagnosis of cardiovascular diseases, treatment of neurological diseases, and intelligent human-machine interaction. As the favorable signal interaction platform of electrophysiological sensing, the conformal property of on-skin electrodes is an extremely crucial factor that can affect the stability of long-term ambulatory electrophysiological sensing. From the perspective of materials, to realize conformal contact between electrodes and skin for stable sensing, highly conformal polymers are in great demand and attracting ever-growing attention. This review focuses on the recent progress of highly conformal polymers for ambulatory electrophysiological sensing, including their synthetic methods, conformal property, and potential applications. Specifically, three main types of highly conformal polymers for stable long-term electrophysiological signals monitoring are proposed, including nature silk fibroin based conformal polymers, marine mussels bioinspired conformal polymers, and other conformal polymers such as zwitterionic polymers and polyacrylamides. Furthermore, the future challenges and opportunities in preparing highly conformal polymers for on-skin electrodes are also highlighted.

A self-powered triboelectric multi-information motion monitoring sensor and its application in wireless real-time control

With the advantages of low power consumption, low cost and miniaturization, self-powered sensors have a wide range of applications in the human-machine interface, Internet of Things (IoTs), sports monitoring and versatile fields. Here, a novel and easy-processing self-powered triboelectric multi-information motion monitoring sensor (TM3S) is proposed and systemically investigated under the conditions of rolling and sliding motion. The relationships between electrical signals (charge, rate of charge change, current, rate of current change, voltage, etc.) and kinetic parameters (displacement, direction, velocity, acceleration, etc.) are established and verified with various motion states. The results show that TM3S can precisely detect the displacement, direction, velocity, and acceleration of the moving object, demonstrating excellent performance in motion monitoring. Furthermore, a wireless self-powered motion monitoring system based on TM3S is developed, which can detect the movement of a sliding mouse in real time, wirelessly transmit sensing data instantaneously, and achieve real-time control in human-machine interface. The real-time motion monitoring system exhibits two significant advantages of high sensitivity and self-powered nature, showing huge potential in low power consumption IoTs and intelligent human-machine interaction.

A flexible dual parameter sensor with hierarchical porous structure for fully decoupled pressure–temperature sensing

• Fabrication of a flexible dual parameter sensor by graphene coated TPU/CNFs sponge. • The sensor has fully decoupled pressure and temperature sensing capabilities. • Excellent pressing durability (>120000 cycles) and temperature detection limit (0.05 K). • Integrated into a robot arm for intelligent human–machine interaction (HMI). Flexible pressure–temperature dual parameter sensors that mimic the function of human skin show potential applications in wearable devices and intelligent robots. Simultaneous sensing of pressure and temperature has been realized by obtaining separated signals utilizing independent piezoresistive and thermoelectric sensing mechanisms, respectively. However, temperature variations induced resistance change poses a negative influence on the pressure sensing accuracy. In this study, a flexible pressure–temperature dual parameter sensor with hierarchical porous structure is presented. The conductivity is realized by inserting graphene into a multilayer structured thermoplastic polyurethane/carbon nanofibers (TPU/CNFs) sponge. The hierarchical porous structured TPU/CNFs sponge with graphene coating endows the sensor with a high pressure sensitivity of 0.14 kPa −1 (0–60 kPa) and a high temperature sensitivity of 30.8 μV/K. Moreover, in the dual parameter mode, the sensor demonstrates neglectable mutual interference between pressure and temperature signals due to lowered temperature-induced resistance change by surface coating of graphene. We show that the sensor can be integrated inside the mask to monitor real-time human respiration. It is also used as the sensing part of a robot arm for high temperature alarm and automatic evacuation. The excellent performance of the sensor ensures its promising application in healthcare monitoring, prosthesis and human–machine interaction (HMI) systems.

Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition.

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

Transition characteristics of driver's intentions triggered by emotional evolution in two‐lane urban roads

Driver's intention is a self-internal state that represents a commitment to carrying out driving action at the next moment, which could be affected by driver's emotion. Therefore, understanding driver's emotion is an important basis for developing driver intention recognition models. This study aims to gain a better insight of the characteristics of driver intention transition trigged by driver's emotion. The Hidden Markov model was used to develop a driver intention recognition model with the involvement of driver's emotions. Assorted materials including visual, auditory and olfactory stimuli were used to evoke driver's emotions before the driving experiments, as well as keep and increase the emotional level during driving. Real and virtual driving experiments were conducted to collect human-vehicle-environment dynamic data in two-lane roads. The results show that the proposed model can achieve high accuracy and reliability in estimating driver's intention transition with the evolution of driver emotion. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.

Driver’s Intention Identification With the Involvement of Emotional Factors in Two-Lane Roads

Driver’s emotion is a psychological reaction to environmental stimulus. Driver intention is an internal state of mind, which directs the actions in the next moment during driving. Emotions usually have a strong influence on behavioral intentions. Therefore, emotion is an important factor that should be considered, to accurately identify driver’s intention. This study used the support vector machine (SVM) theory to develop a driver intention recognition model, with the involvement of driver’s emotions. Various materials including visual materials, auditory materials, and olfactory materials, were used to induce driver’s emotions. Real driving, virtual driving and computer simulation experiments were conducted to collect human-vehicle-environment dynamic data in two-lane roads. The results present that the proposed model can achieve high accuracy and reliability in recognizing driver’s intentions. Our findings of this study can be used to develop the personalized driving warning system and intelligent human-machine interaction in vehicles. This study would be of great theoretical significance for improving road traffic safety.