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Indirect Tire Pressure Monitoring System (i-TPMS) monitors inflation pressures in pneumatic tires using wheel speed sensor signals acquired from Anti-lock Braking System (ABS). In order to monitor the tire pressures indirectly, frequency analysis and wheel radius analysis are operating in parallel. This manuscript focuses on the wheel radius analysis. Changes in vehicle weight can influence the difference in rolling radius of the front and rear tires. In this manuscript, a tire rolling radius compensation algorithm using a longitudinal acceleration (Ax) sensor is proposed, without any additional cost. The Ax sensor offset caused by loaded mass is estimated when the vehicle is standstill as well as in motion. The tire rolling radius can be compensated using the estimated offset of the Ax sensor. The compensation amount of the tire rolling radius can be obtained based on the difference between the estimated value from the TPMS learning phase and the value from the TPMS detecting phase. Vehicle tests have been conducted in order to evaluate the proposed estimation algorithm. It has been shown from the test results that the proposed method provides satisfactory performance for compensating the tire rolling radius for the changes in vehicle weight.

Tire Pressure Monitoring Systems (TPMS) are electronic wireless systems that monitor and report air pressure inside pneumatic tires in real time. An example of a TPMS module integrated with the valve stem and showing the typical tire mounting location is shown in Figure 1. For their safety and fuel economy benefits, starting with the mid-2000's, active TPMS were mandated on many vehicles worldwide. The NHTS estimates that there are approximately 23,000 accidents and 535 fatalities annually involving tire underinflation and blowouts [1]. The use of TPMS has been shown to result in improved fuel economy and therefore reduced carbon emissions [2]. TPMS in passenger vehicles was mandated in the US as of Sept. 1, 2007 under the TREAD Act, in the European Union as of Nov. 1, 2012 and in South Korea as of Jan.1, 2013. Countries like Russia, Indonesia, the Philippines, Israel, Malaysia, Turkey and many others soon followed [3]. The first TPMS systems were large and bulky with a significant electronics content [4]. Since that time TPMS electronics have gotten more energy efficient and form factors have come down dramatically. This paper will outline an effort to miniaturize an existing 1.0 mm pitch, 7x7x2.2 mm body size 24 lead QFN (Quad Flat No Leads) TPMS down to a 4x4x1.98 mm body size QFN with 0.5 mm pitch that would still meet automotive AEC Grade 1 reliability requirements. The original 7x7 mm three die QFN package consisted of an ASIC, a pressure sensor and an accelerometer. This miniaturization led to many technical challenges at both the package and board level. This paper will primarily address the board level reliability (BLR) challenges encountered due to the large silicon to package ratio along with the 50% reduction in pitch. Through a series of test vehicles with variables such as QFN leadframe surface finish, lead shape and size, wettable flank (WF) technology and anchors pads, the BLR was successfully improved to the point where it met application requirements.

Tire pressure monitoring system (TPMS) has been brought into the stringent regulatory frameworks of many countries in the automotive market. However, common commercial TPMSs must rely on a life-limited battery, which brings about some safety risks and environmental problems. It is worthy to note that energy harvesting approaches are promising to realize a batteryless TPMS. In this article, a rotation-driven piezoelectric energy harvester with eight typical nonlinear buckled bridges is proposed to effectively scavenge ultralow frequency kinetic energy. Thinned bulk PZT film is employed as the piezoelectric functional layer based on its excellent electromechanical factor. Gear-induced interwell oscillation mechanism ensures the effective deformation of the piezoelectric buckled bridges. The developed harvester can generate the effective output power of 8.9 mW under the optimal resistance of 3 kΩ at 8.3 Hz rotational frequency. The 47, 100, and 330 μF capacitors can be saturated at 14.5, 15.1, and 14.5 V during 10, 19, and 51 s, respectively. Additionally, a commercial TPMS can be effectively powered by this harvester when it works at more than a critical frequency locating at 3.0–3.7 Hz. The commercial TPMS can be operated in realtime when the applied rotational frequency is more than 8.3 Hz.

Indirect tire pressure monitoring system monitors inflation pressures in pneumatic tires using wheel speed sensor (WSS) signals acquired from anti-lock braking system. In order to monitor the tire ...

In order to meet the requirements for high sensitivity and low temperature drift of pressure sensors in the field of meteorological detection or tire pressure monitoring system (TPMS), this paper proposes a MEMS chip-level pressure sensor based on two pairs of aluminum-silicon hybrid structures, which are etched on the SOI wafer. The pressure sensor achieves the amplified piezoresistive effect by the micron thick ohmic contact between the boron-doped silicon and external aluminum shunt, as verified by finite element numerical simulation and experiment. Four symmetrical L-shaped raised structures are distributed around the square strained membrane, which enhance the stress of the pair of stress-sensitive aluminum-silicon hybrid structures. The other pair of hybrid structures placed outside the strained membrane of the sensor chip enables differential sensor output which is highly insensitive to temperature. The offset drift of differential output is reduced to −4.39%FS compared with that of single aluminum-silicon hybrid structure. Cooperated with temperature compensation structures, an additional thermodynamic control system by using PID algorithm to keep the working temperature stable can eliminate the temperature drift of sensor output. The feasibility of the thermodynamic control system is verified by steady-state thermal simulation. Experiment test results show that the sensitivity of a single aluminum-silicon hybrid structure can reach 0.283 mV/V/kPa at constant heating temperature of 50 °C, and whose thermal coefficient of offset (TCO) can be reduced from −6.92E-1 to −1.51E-3 %FS/°C in ambient temperature ranges of −20 to 50 °C.

As you know, a radical means to prevent skidding of the vehicle (TK) during braking is the use of such integrated automatic active safety systems as anti-lock braking system (ABS), emergency brake booster, traction control system (TRC), electronic brake force distribution system EBD), stability control system (VSC), tire pressure monitoring system (TPWS), electronic control brake system (ECB), electric power steering (EPS), integrated dynamic vehicle control system (VDIM). Also, the stable position of the vehicle when driving on the road is provided by a set of automatic devices (for example dynamic stabilization system, anti-lock and anti-slip systems, etc.). Most of the cases of violation of the stable position of the vehicle on the road are related to the process of its braking. The article considers an alternative approach to stabilizing the position of the vehicle on the road during braking due to another approach to the management of its braking system. The mathematical description and schemes of position of the vehicle in the course of braking are offered. The stability of the position of the vehicle is ensured by braking the rear wheels, or braking one of the rear wheels (internal in relation to the direction of skidding), due to the system of dynamic stabilization of the course angle. Braking of the rear wheels during the initial skidding during braking allows you to stabilize the course angle of the vehicle (with full braking of the rear wheels, the stabilization time is minimal). This significantly reduces the braking efficiency of the vehicle, as only the front wheels are brake. Braking of only one rear wheel allows to provide identical duration of transition process at the highest efficiency of braking of the vehicle. On the example of a conventional vehicle, a comparative analysis of the effectiveness of the methods of dynamic stabilization of the course angle by braking one and two rear wheels.

This paper describes a novel low-cost tire pressure monitoring system (TPMS) for bicycles. The system is based on a battery-less near field communication (NFC) tag that integrates a pressure sensor, which is placed between the tire and the inner tube. The system uses no battery since the energy is obtained from the magnetic field induced by the reader in order to establish the communication. The pressure can be read by any NFC-enabled reader such as a smartphone. The pressure sensor is a force-sensing resistor (FSR) that controls the oscillation frequency of a 555 timer-based oscillator. The frequency is read and computed by a microcontroller, and the result is written via $\text{I}^{2}\text{C}$ bus to the memory of a near-field communication integrated circuit (IC), which is read by a commercial smartphone or NFC reader. A prototype is designed on an FR4 substrate which integrates a $12 \times 16$ mm antenna connected to the NFC IC (NT3H211 from NXP). The proposed prototype has a 3D-printed enclosure to protect the components mounted on the surface of the PCB and to protect the tire from punctures. A system for characterizing the read range is presented. The tag requires a minimum magnetic field of $2.9~A_{RMS}/m$ to operate up to a distance of 8 mm between reader and the tire, providing enough power to supply energy to the whole circuitry and to receive the information stored at the NFC IC. The system has been tested with a 59 mm-width mountain bike tire. The sensor has been characterized using a simple polynomial fitting to obtain the pressure from the measurement of the oscillation frequency. An accuracy of 0.1 has been accomplished in the range of 0.5 to 2.2 bar. To test the system, several inflation cycles have been performed and showed that the system presents good repeatability.

Pressure of tire is measured using Tire Pressure Monitoring System (TPMS) model. By measuring the tire pressure, the fuel can be saved and the vehicle can be controlled in better way. The real of tire pressure can be monitored in real time and if the pressure of the tire goes out of the threshold this system gives alarm and alert the driver to ensure the driver safety, hence the life of the driver can be safeguard and pre-warn the driver and passengers. TPMS consists of an electronic unit that sense the tire pressure displays it to user in a LABVIEW platform. This unit includes a pressure sensor and Data Acquisition System. The output of the pressure sensor is given to the Data Acquisition System. Then the DAQ is interfaced with PC to display the tire pressure in the dashboard using the LABVIEW platform. If the tire pressure crosses safe pressure level a warning is generated

This study aims to experimentally examine the effects of vehicle speed changes on the vertical resonance frequency of effective rolling radius variations for applying to TPMS (Tire Pressure Monitoring System). For this, the road tests have been conducted at constant vehicle speeds 30, 60, 90km/h for tire pressures 15, 20, 25, 30, 35psi. In these tests, the effective rolling radius changes have been measured. Test results show that the vehicle speed changes cause the resonance frequency to shift at same tire pressure. Therefore, this study shows that the changes in vertical resonance frequency resulting from vehicle speed changes may cause wrong tire pressure information to be taken by TPMS.

Modern vehicles which have internal sensor networks are one of the examples of a cyberphysical system (CPS). The tire pressure monitoring system (TPMS) is used to monitor the pressure of the tires and to inform the driver of them. This system is mandatory for vehicles in the US and EU. To ensure the security of TPMS, it is important to reduce the cost of the cryptographic mechanisms implemented in resource-constrained devices. To address this problem, previous works have proposed countermeasures employing lightweight block ciphers such as PRESENT, SPECK, or KATAN. However, it is not clear to us that any of these works have addressed the issues of software optimization that considers TPMS packet protection as well as session key updates for architectures consisting of the vehicle TPMS ECU and four low-cost TPMS sensors equipped with the tires. In this paper, we propose the application of ISO/IEC 29192-5 lightweight hash function Lesamnta-LW to address these issues. When we apply cryptographic mechanisms to a practical system, we consider the lightweight crypto stack which contains cryptographic mechanisms, specifications for the implementation, and performance evaluation. Our approach is to apply the known method of converting Lesamnta-LW to multiple independent pseudorandom functions (PRFs) in TPMS. In our case, we generate five PRFs this way and then use one PRF for MAC generation and four for key derivation. We use the internal AES-based block cipher of Lesamnta-LW for encryption. Although we follow the NIST SP 800-108 framework of converting PRFs to key derivation functions, we confirm the significant advantage of Lesamnta-LW-based PRFs over HMAC-SHA-256 by evaluating the performance on AVR 8-bit microcontrollers, on which we consider simulating TPMS sensors. We expect that our method to achieve multiple purposes with a single cryptographic primitive will help us to reduce the total implementation cost required for TPMS security.

The objective of this paper is to detect the car driver behavior. Many factors can influence the behavior of the driver that includes fatigue, distraction, eye-blinking, taking eyes off from the road, time of the driving etc. The in appropriate behavior while driving the car can leads to accidents that in return damages one’s life or the vehicle. The papers discuss the various techniques to monitor the driver behaviors and describes the advantages and disadvantages of the methods used to detect the driver behavior in a car.

Tire pressure monitoring is essential to driving safety. Indirect tire pressure monitoring system (TPMS) is a cost-effective alternative to direct tire pressure monitoring system. Its performance depends on the algorithm for data pre-processing and analysis which is normally complicated, sensitive to initial calibration with limited working range. In this work, four tests were carried out with Baojun 530 with a different deflated tire in each case. Speed data read through ABS CAN bus was analyzed and traditional frequency based method was employed to identify the deflated tire. To simplify the data pre-processing and improve response speed and working range, a new artificial neural network (ANN) based method was also proposed to identify deflated tire based on speed data point collected through antilock brake system (ABS) sensors in tests. A long short-term memory (LSTM) network was developed to locate the deflated tire with an accuracy of 0.83 after training for individual data points. And performance of this method can be further improved by employing a soft voting mechanism with 3 LSTM networks. In proposed ANN based method, benchmark data from properly inflated tire is not required, which makes it a promising solution for multiple deflated tires cases which is challenging for traditional frequency-based method.

Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.

This paper presents a new indirect tire pressure monitoring system (TPMS) based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested methodology is based on the explicit correlation between tire pressure and tire stiffness and is available in real time. AEKF-UI is used to simultaneously estimate the time-varying parameter (tire stiffness) of vehicle suspension systems and the road roughness using an unknown input estimator. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate tire stiffness (i.e., tire inflation pressure) variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm are verified through a laboratory-level experiment. This study offers a potential application for an alternative indirect TPMS and the estimation of unknown road roughness used for automotive controller design.

In recent years, the development of IT systems for fleet monitoring was observed. Tire pressure monitoring systems are constantly improved. Decreased values in tire pressure can cause deformation of tires. Monitoring of tire pressure is an important function in oversized transport trucks. Tire pressure and rolling resistant influence fuel consumption. The purpose of this paper was to determine the impact of tire pressure on fuel consumption in a fleet of trucks with tire pressure monitoring system installed and to determine the impact of other factors that may affect fuel consumption, such as the vehicle weight, brake usage and cruise control usage. The results of the research were developed using a multiple regression model describing the above dependence.

Currently, the Tire Pressure Monitoring System (TPMS) only monitors the condition of a tire pressure. However, there are no particular reactions taking place after the value of its tire pressure is discovered. In fact, the value of a tire pressure determines driving comfort and safety Therefore, this research proposed a method to integrate a TPMS and a Pressure Sensor Base (PSB) with a particular reaction required to fulfill tires automatically. The proposed TPMS has an electronic device unit directly attached to a tire’s valve. This unit includes pressure sensors, microcontrollers, Bluetooth transmitters and batteries. An alert system is generated whenever tire pressure exceeds the maximum or minimum safe pressure level. Moreover, if the pressure measured is below the lowest level of the desired pressure, it will automatically activate the compressor. Several experiments have been carried out to analyze the proposed system. The integrated TPMS has proven to be able to be an alternative tool for the automotive sector to keep maintaining the tires and to improve a driving comfort and safety.

This research aims to develop an energy-harvesting device that converts kinetic energy in rotating environments into electrical energy which can be used as a power source for a tire pressure monitoring system (TPMS) to replace chemical batteries. An energy harvester with a weighted trapezoidal cantilever that had the ability to self-adjust its resonant frequency approaching to the wheel rotation speed to keep a resonant mode in order to generate a higher amount of output power was developed. The weighted length of the trapezoidal cantilever was tuned by the centrifugal force from a rotating wheel to vary the area moment of inertia at the cantilever root. The dynamic equation was derived through the Lagrange method. Moreover, the weighted cantilever can also be considered as a centrifugal pendulum vibration absorber (CPVA) to absorb unexpected vibrations or torque. Therefore, this study analyzed the dynamic behavior of the wheel when a disturbing torque was applied to it. The experimental results revealed that the output power of the prototype was approximately 65–110 μW at the wheel rotation speeds in the range of 240–1200 rpm. This demonstrated that the proposed device has the potential to replace the battery in the TPMS. The effective vibration absorption bandwidth of the prototype was approximately 130 Hz, and more than 42% of the vibration absorption ratio was observed under disturbing vibration between 60–110 Hz.

Modern vehicles are equipped with vehicular sensors for smart navigation, vehicle state awareness, and other intelligent operations. Despite the previous belief that the sensor operations stay within a vehicle, as it is designed to be, we study information leakage through the tire pressure monitoring system (TPMS) sensors and the corresponding privacy breach. We demonstrate that, using a low-cost and off-the-shelf software defined radio (SDR), an unauthorized attacker can track uniquely-identifiable sensor IDs up to 40 meters away from the vehicle. To address the issue and protect vehicular privacy, we also propose an effective and lightweight TPMS ID randomization scheme and analyze its security and the implementation costs.

Automobile instrument clusters these days are equipped with numerous warning icons for informing drivers on different critical automobile functions. In this context, abstract and complex icon designs can often be misinterpreted. Use of icons that do not reveal their functionality through their visual characteristics increases the risk of road accidents. Designers often follow relevant guidelines before designing usable warning icons. To warning icons namely ‘Brake Warning Light’ (BWL) and ‘Tire Pressure Monitoring System’ (TPMS) were found to be the poorly usable icons in past research work by the authors and other researchers. The intent of this study is to design and evaluate usable alternative designs for these two icons in the Indian context. Standard available guidelines and Peirce’s model of semiotics were adopted during this work. 50 participants volunteered in the first study to report their respective concerns with the two icons. Four alternatives each for the two icons were designed and in a second study, they were rated by 60 volunteers on the perceptual factor of ‘semantic closeness’.

In the past few years, automobile dashboard and its instrument panel are becoming increasingly complicated as they offer a variety of digitized functionalities such as warning icons. Past research shows that certain automotive warning icon designs could be hard to interpret by drivers. A study was undertaken for determining the usability of certain standard warning icons in automobile instrument clusters using system usability scale (SUS) and also to investigate the “guessability” of these icons. Here, a set of 20 standard warning icons which are commonly used in new models of Indian cars were selected from ISO 2575:2010, and 50 participants were employed to determine the usability and guessability of these icons. Standard designs of two car dashboard warning icons, namely “Brake Warning Light” and “Tire Pressure Monitoring System” were found to be problematic. A significant and strong positive correlation was found between the mean guessability score and the mean SUS score for all the 20 warning icons.

This paper proposes a method combining bandpass filter and adaptive notch filter to remove interference signals generated by engine ignition frequency. Firstly, The report analyzes the interference of engine ignition frequency to the system in the process of implementing the indirect tire pressure monitoring system; then introduces the relationship between the engine's interference frequency and speed. At last, according to the characteristics of the interference frequency and useful signal, a method combining bandpass filter and adaptive notch filtering is proposed to remove the interference frequency. The experiment shows that frequency interference exists, and the method proposed in this paper can obviously reduce the influence of interference frequency.

Advanced driver assistance systems (ADAS) refer to technologies that automate, facilitate, and improve systems in the vehicles in order to assist drivers for better and safer driving. There are several ADAS technologies such as adaptive cruise control (ACC), lane departure warning systems, forward collision warning systems, traffic signal recognition system (TSR), tire pressure monitoring system (TMPS), night vision, pedestrian detection, parking assistance systems, automatic emergency brake systems, driver behavior monitoring, blind spot detection, electronic stability control (ESC), alcohol interlock systems, etc. Some of the ADAS technologies are intended for safety improvement, and some others are for convenience function. This chapter explains each of the different ADAS technology in detail with their deployment details. The development and deployment of these technologies relies mainly on the embedded systems and advanced signal processing technologies such as multiple signal classification (MUSIC) and light detection and ranging (LiDAR).

The active safety technology of vehicles has become a hot research topic nowadays, among which the intelligent tire is an important field. The existing tire pressure monitoring system (TPMS) however, cannot provide a comprehensible supervision for the tires. This paper proposed an intelligent tire information system, which can estimate the tire abrasion depth, based on triaxial accelerometer data and strain gauge data. The system consists of different sensors in the data acquisition section, a controlling chip in the car terminal and the display section on mobile devices. Based on the data analysis from the sensors and multiple useful features extracted from the data, a BP neural network is established to training an abrasion depth prediction model in MATLAB. At last, we conduct experiments to verify our models with 943 collected data and compare the model with the multivariable linear regression model. The absolute errors of randomly chosen testcases can all keep within ±0.5mm and about 80% of them lies in the range of ±0.2mm, which shows the efficiency and reliability of our system and prediction model.

A multi-level comprehensive evaluation model of rollover prevention performance for commercial vehicle is proposed for the first time in this paper. By analyzing the performance indexes and test conditions of four active prevention and control technologies, including Driver Status Monitor (DSM), Curve Speed Warning System (CSWS), Electric Stability Control (ESC) and Tire Pressure Monitoring System (TPMS), the weights of their technologies and test conditions are assigned, and the test conditions are scored. Combined with the weights and test scores, the total score of rollover prevention performance is obtained according to the evaluation model formula. Then, the rollover prevention performance is ranked. Finally, the validity of the model is verified by the test data of three test vehicles, which can evaluate the overall rollover performance of commercial vehicle comprehensively and scientifically.

This paper presents a new estimation method of tire stiffness based in improved Kalman filter of vehicle suspension control system. In recent years, the need for systems monitoring the current pressure in pneumatic tires has grown dramatically. Incorrect pressured tire will affect the handling performance, tire life time and fuel economy. For these reasons, tire pressure monitoring system(TPMS) is required to ensure the vehicle safety and ride quality. However, traditional TPMS requires a battery in each tire in order to power the sensor and circuits inside the tire and it has temperature dependent capacity problem. To overcome this problem, indirect methods are proposed. One of the promising indirect methods is the sensor fusion method from automotive control systems. In this study, adaptive extended Kalman filter(AEKF) approach is proposed to identify structural parameter, such as tire stiffness. Simulation results demonstrate that proposed approach is capable of estimating tire pressure based on experiment of relation between tire pressure and tire stiffness.

The tire as the only contact between vehicle and road surface has a significant influence on vehicle safety, ride comfort, driving dynamics and fuel economy. A tire can reach the optimum performance, only when an appropriate tire pressure has been adjusted. Due to the importance of tire pressure, the USA has published a legal regulation (FMVSS 138) to require that tire pressure monitoring systems have to be installed in light vehicles since 2007.

Nowadays, modern vehicles possess many sensors to analyze the surroundings like meteorological data or the position of other road users. The received data are used to improve the safety and comfort by different advanced driver assistance systems. Some studies forecast a rapid growing spread of electric and autonomous vehicles in the future. Hence, the number of sensors will increase to realize e.g. autonomous vehicles of level 4 and 5. One important parameter for the adjustment of the driver assistance systems is the vehicle mass. This bases on the not negligible influence of the vehicle mass on the kinetic energy of vehicles. The paper presents a smart tire pressure monitoring system that is able to detect the vehicle mass by the use of piezoresistive pressure sensors. In the everyday use of the presented system, there are various parameters of influence, which affect the accuracy of the vehicle mass detection. Thus, the paper shows the analysis results of the application of different digital filters to enhance the accuracy of the developed system. In addition, the new prototype with Bluetooth 5 is presented and an analysis of the energy consumption is done.

This paper presents an energy-efficient wireless transmission scheme for battery-less vehicle tire pressure monitoring system (TPMS). Our proposed transmission scheme includes a wake-up communication link with 125-kHz carrier frequency and a data communication link with 433-MHz carrier frequency. Considering the TPMS application requirements and the special nature of the vehicle environment, we derive the relevant circuit parameters. In order to verify the reliability of the wireless communication system under the derived circuit parameters, we design an in-tire data transmitter and wake-up receiver. The 125-kHz wake-up receiver adopts dual-channel to improve the communication reliability and logarithmic amplifiers to achieve ASK demodulation and dynamic range compression. The receiver is implemented in 0.35- $\mu \text{m}$ high voltage (HV) BCD process. Experiment results show that typical power consumption of the receiver is no more than $5~\mu \text{A}$ under 3.3 V supply voltage; the maximum data rate is 35 kb/s with 0.5 mVpp sensitivity. On the other hand, the data transmitter is implemented in 0.18- $\mu \text{m}$ MMRF process. Experiment results show that the typical power consumption is 7.3 mA under 1.8 V supply voltage, and the emission power is −10 dBm@433.92 MHz with a phase noise of −103 dBc/Hz@300 kHz. System level experiments demonstrate that the proposed wireless transmission scheme fulfills the vehicle TPMS requirements. The data transmitter and wake-up receiver can communicate with the commercial data receiver and wake-up transmitter in the range of 20 m, which meets the requirements of most vehicles.

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum–silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum–silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum–silicon hybrid structures are located inside and outside the strained membrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from −20 °C to 100 °C, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ±0.91 °C, −1.225 mV/(V °C) and −0.150% °C−1. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from −3.553%FS °C−1 to −0.375%FS °C−1 based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The TCO is −0.017%FS °C−1, and the thermal coefficient of span is −0.020%FS °C−1. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.

Tire mileage and wear provide important information for vehicle applications. There are more and more studies discussing intelligent tires, but few focus on the role of tire mileage and wear. The conventional tire pressure monitoring system (TPMS) is one of the intelligent tire applications, but there has been no significant advancement in recent years in this regard. In order to increase the additional functions of intelligent tire applications, we propose a method that estimates the mileage and wear information of tires. The proposed method uses a three-axis sensor and a Hall sensor to implement the function. The proposed method also has a low power design to reduce the power consumption of the Hall sensor. The experimental results show the trend of tire wear status, rendering this method effective. This method also requires more accurate mileage information to support tire wear estimation. This experiment found that the correct rate of the proposed mileage estimation method is 99.4% and provides sufficient and correct mileage information for tire wear methods. If this method is used in autonomous vehicle applications, the autonomous control strategy algorithm has more conditions to plan the control strategy. The strategy system processes more meticulous control that increases the safety of autonomous vehicles.

Since under-inflated wheels may lead to unsafe driving conditions, in many countries all the new cars must be equipped with tire pressure monitoring systems (TPMS). Such devices can be “direct”, in case air pressure sensors are employed, or “indirect”, if low tires are detected by comparing relative wheel speeds, typically via already available speed sensors. While the former are perfectly suited but costly, the latter are cheaper but they typically cannot be used to detect four deflated wheels. In this work, we present a “hybrid” solution, which is able to estimate the pressure of all four wheel by employing a single pressure sensor. Then, such a solution turns out to be a good trade-off between the systems cost and the measurement performance. The effectiveness of the approach is illustrated via experimental tests carried out on a real vehicle setup.

The tire pressure monitoring system (TPMS) research is mostly concentrated in the ultralow-power circuit field. However, one of the biggest challenges is overcoming the unavoidable replacement of batteries owing to their self-discharging, unstable performance by using the energy-harvesting technology. A TPMS must be installed on the wheel surface for permanent operation. Likewise, the energy harvester should be directly installed inside the TPMS circuit to eliminate any additional maintenance. Thus, the size of the energy harvester for independent rotational systems, such as a tire wheel, should be constrained to the battery level. Electromagnetic induction was selected to maximize the power generated by the energy harvester. The optimized model was rationally examined, and the results were compared with the calculated results. In preliminary modeling of the mechanical energy transduction into electrical energy and the parameters in the m-c-k system, the module was predicted to generate about 136 μW of consumable power. A test module was fabricated for practical application. With the experimental constraints, the maximum power consumption was 195 μW under the conditions of a base excitation of 12 Hz and acceleration of 0.5 g for a system volume of about 2.5 cm3.

Since the National Highway Traffic Safety Administration mandated the incorporation of tire pressure monitoring systems (TPMSs) in all newly produced passenger vehicles, most vehicle manufacturers have adopted direct pressure measurement. Direct TPMS sensors embedded in each tire require a wireless radio frequency (RF) communications link that broadcasts tire status to the vehicle once per minute from each tire when at speed. Each TPMS message communicates benign information that includes pressure and temperature as well as a static unique identifier that may be exploited, which raises concerns about privacy and spoofing. To focus on concerns related to the TPMS-RF interface, vehicle motion simulations were integrated with live propagation modeling measurements from three classes of passenger vehicles: subcompact car, full-size sedan, and full-size pickup. The RF link and channel models for this TPMS interface with the vehicle resulted in surprisingly long ranges away from the vehicle for the radiation of the unique identifiers. A TPMS sensor redesign could use the proposed RF channel propagation measurements to change the directions of signal propagation while reducing battery consumption by the TPMS sensor (which is affected primarily by RF transmission).

The automotive sector is currently in a dynamic change by the introduction of new vehicle concepts, like electric and autonomous vehicles, and innovative driver assistance systems like systems for a precise adaptive headlight adjustment. The continuous adjustment of the headlamps is very important for the correct illumination of the road and prevention of blinding the oncoming traffic, respectively. Hence, a great share of new vehicles possess an automatic headlight adjustment. In some countries, like Germany, these systems are legally required for some kinds of headlights. Nevertheless, there are only a few suppliers for aftermarket products and thus, the product range of automatic headlamp adjustment systems for existing vehicles is low. This paper presents a system concept for the aftermarket that is modular designed and easy to integrate in existing vehicles. In addition, the concept includes a novel approach by the application of a smart tire pressure monitoring system for the detection of the tilt angle of the vehicle. Therefore, consumers who like to change their light technology (e.g. LED instead of halogen headlights) and to integrate a system for headlight adjustment can benefit from the system shown in this paper.

Tire pressure monitoring system (TPMS) is used to detect the tire pressure, temperature and many other parameters in real time. At present, most of these systems are powered by button batteries, which cannot last the entire lifetime of the device and periodic replacement or recharging is tedious. As a promising alternative method for batteries, harvesting energy from tire vibration has attracted widespread attention in recent years. In this paper, the research progress of energy collection for TPMS application is reviewed. Three transduction mechanisms used for tire vibration energy harvesting are focused and the output performance of energy harvesters are compared. The results of literature research show that the piezoelectric energy harvester is most suitable for TPMS power supply and in the design of energy harvester, not only consider the installation location, suitable speed range, energy requirements, but also pay attention to temperature, cost and other factors.

A novel on-chip integration of pressure plus 2-axis (X/Z) acceleration composite sensors for upgraded production of automobile tire pressure monitoring system (TPMS) is proposed, developed, and characterized. Herein, the X-axis accelerometer is with the cantilever beam-mass structure and is used for automatically identifying and positioning each of the four wheels. The IC-Foundry-Compatible low-cost batch fabrication technique of MIS (i.e., Micro-openings Inter-etch and Sealing) is employed to only fabricate the device from the front side of (111) silicon wafer, without double-sided micromachining, wafer bonding, complex Cavity-SOI (Silicon on Insulator) processing, and expensive SOI-wafer needed. Benefited from the single-wafer front-side fabrication technique on ordinary single-polished wafers, the fabricated composite TPMS sensor has the advantages of a small chip-size of 1.9 mm &times, 1.9 mm, low cross-talk interference, low-cost, and compatible process with IC-foundries. The fabricated pressure sensors, X-axis accelerometer and Z-axis accelerometer, show linear sensing outputs, with the sensitivities as about 0.102 mV/kPa, 0.132 mV/kPa, and 0.136 mV/kPa, respectively. Fabricated with the low-cost front-side MIS process, the fabricated composite TPMS sensors are promising in automotive electronics and volume production.

Tire pressure as an important vehicle parameter has a great impact on the vehicle safety, ride comfort, driving dynamics and fuel economy. Every registered vehicle in the USA needs to be equipped with a tire pressure monitoring system from 2007. This legal requirement has been published in the EU since 2012.

In recent years, the topic vehicle mass estimation has become more and more popular. One of the reasons for this development is the growing spread of electric vehicles. The advantages of a precise vehicle mass estimation for owners of electric vehicles are e.g., a reliable range prediction or the selection of energy efficient routes depending on the current payload and state of charge. This paper presents a novel approach to estimate the vehicle mass by monitoring the tire pressure in combination with a two-stage step detection and a modified Kalman filter. The so-called Smart Tire Pressure Monitoring System offers many chances to enhance energy efficient driving strategies or advanced driver assistance systems. The presented paper shows the results of a large-scale test series of the Smart Tire Pressure Monitoring System. Based on these results, the second part of the paper clarifies the gained improvement of the range prediction by the detected vehicle masses. The analysis of the energy consumption of a Mitsubishi i-MiEV with four own driving cycles highlights the potential for improvement for the range prediction in detail.

Recent studies have shown that ‘’hacktivists’’ can mount serious attacks on automobiles. The automotive On-Board Diagnostic (OBD) interface enables an efficient way to access information of the in-vehicle electronic system and leaves way for unauthorized access by an intruder. We discover that remote exploitation is feasible via a broad range of attack points such as mechanic tools, CD players, Bluetooth and Tire Pressure Monitoring System. Wireless communications channels allow long distance vehicle control. Finally, the paper discusses the drawbacks of Seed-Key Mechanism to authenticate and provide an extra layer of authentication to help build a safer automotive ecosystem.

This paper presents the tire pressure monitoring system (TPMS) by using the system on chip (SoC) mixed signals with the help of Bluetooth transmission and in advantage of low power consumption design. This is to monitor the variations in temperature and pressure of the vehicle’s tire, and the TPMS system is involved. It improves the driver’s safety by automatically detecting the tire pressure and temperature and then warning signal is sent to driver to take a measure, which prevents from accident. The proposed system of tire pressure monitoring system using SoC increases the speed of indication time to the driver by using mixed signals. The inflation of the tire can be avoided by preventing from high temperature and high pressure. Limitation of temperature and pressure in the previous system is also elongated i.e. temperature from 40℃ to 125℃ and pressure from 0 to 750 Kpa. Sensors, wireless communication (Bluetooth dongle) and SoC unit are used to design the low power TPMS. Quantitative results are taken and the analogy between temperature and pressure is also verified. The tested results proved by need of the practical system. Signal conditioning voltage and SoC unit is the trace for low power design TPMS. Finally, the performance of the system is tested and executed by using proteus software given as a real time application.

Automobile puncture is one of the major hidden dangers of frequent traffic accidents. Accurate and real-time monitoring of tire pressure of automobile tires is of great significance for improving vehicle safety and improving traffic status. This paper first summarizes and analyzes the published technical methods of automobile tire pressure detection, and analyzes the advantages and disadvantages of related detection methods in combination with previous research results. Finally, this paper proposes a development direction of the tire pressure system. The tire pressure and tire temperature signals obtained by the tire pressure monitoring system are used as the correction amount of the ABS system control signal, and the tire pressure and the tire temperature can always be controlled within a safe range. It can also reduce the chance of tire’s bursting due to sudden braking and improve the reliability of the ABS system.

In order to improve the accuracy of the pressure and temperature data measured in the tire pressure monitoring system(TPMS), an optimized Bayesian estimation data fusion method is proposed. The systematic scheme is designed, which can fulfill the requirements of system function. The Bayesian estimation is used to fuse the multi-sensor data to reduce the uncertainty of the measurement. Combining the Kalman filter eliminates the noise signal to obtain reliable data information. Experimental results show that the proposed algorithm can effectively suppress noise and take precise pressure and temperature values.

The aim of this study is to explore whether risk perception or anticipated regret is responsible for intensifying the participants’ intention to adopt a tire pressure monitoring system (TPMS) to prevent a tire-related accident, and whether the optimism bias has a moderator effect between risk perception/anticipated regret and intention. With 274 valid questionnaires and PLS-SEM (partial least squares structural equation modeling) analysis, the results indicate a significant positive relationship between risk perception and intention to adopt TPMS, but not between anticipated regret and intention. The moderator effect of optimism bias on risk perception and anticipated regret is not found in the model. The findings will prove useful for public service advertising campaigns by providing a basis for an understanding of the role of cognitive and emotional factors in tire-blowout accident prevention, thereby increasing the motivation for drivers in Taiwan to take advantage of the protection afforded them by using TPMS.