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Structure of the prototype TPMS receiver [own work]. 

Structure of the prototype TPMS receiver [own work]. 

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Conference Paper
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In this paper we present initial results in utilization of TPMS (Tire Pressure Monitoring System) for collecting traffic data and deriving traffic information, i.e. travel times. The obtained results show that current detection ratio is less than 5 % and the obtained travel times are in consistency with referent data. The experiment is performed on...

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Context 1
... This approach provides high flexibility and possibility to implement parallel processing chains for different sensor types without multiplication of hardware. For this purpose, USRP device from Ettus Research [14] was used as radio frontend, while the software is based on GNU Radio framework [15]. The block diagram of the receiver is shown in Fig. 3. As can be seen, there are two main processing paths based on modulation type. One processing path is used for sensors which utilize ASK modulation, while the other is used for FSK modulation. Our receiver is capable of receiving signals from seven different types of TPMS sensors and measured range is around 100 m when directional ...
Context 2
... the focus here. Direct TPMS, in the following text referred only as TPMS, use the sensors that are measuring absolute pressure and temperature values inside the tires. The operation of TPMS has already been described in [8, 10] and only brief description will be given here. The sensors are installed on the rim, usually inside the tire. The connection between sensors and ECU (Electronic Controlling Unit) is established via receiving unit, using radio link. Sensors transmit their data periodically. The typical value of the transmission period is about 60s, but it can differ from manufacturer to manufacturer. TPMS intended for usage in the EU uses 434 MHz ISM band, while in the US, it is also allowed to use 315 MHz band. The data sent from sensors to the receiving unit is organized in frames. Every data frame consists of the following fields: preamble, sensor ID, pressure, temperature, status and error correction data. The receiving unit decodes messages sent from the sensors and provides corresponding information to the ECU, which informs the driver about the state of the pressure. The structure of TPMS is shown in Fig. 1. TPMS based traffic data detection relies on receiving TPMS signals externally and using information about sensor ID as an identifier of the vehicles. This idea is described in [8], while the practical results on eavesdropping TPMS is presented in [12]. For deriving traffic information using TPMS we assume sensor ID is unique, and it can be used for vehicles identification. The assumption is based on the fact that sensor ID is 32-bit number, which allows 2 32 unique values. This value is much bigger than the number of produced vehicles in the in the period of the lifetime of the sensor which is usually reported as ten years. There are several benefits of using TPMS collecting traffic data. First, as stated earlier, TPMS usage will become mandatory for all new vehicles in the EU and many other countries in the near future, which means that no additional in-car hardware will be required. Second, the usage of TPMS is limited only to vehicles, and faulty detection of cyclists and pedestrians is not possible like with Bluetooth detection [13]. Third, weather conditions do not have negative influence on the detection as in ANPR technology. On the other hand there are several limitations of TPMS approach. First, the strength of the radiated signal is constrained by the strict EMC regulations. Second, there is no standard for communication between sensors and the receiving unit which results in many different realizations of TPMS. The principle of TPMS detection is shown in Fig. 2. In order to collect TPMS data from the traffic and use them for deriving traffic information, the following was done: • Analyzing TPMS sensor operation • Designing receiver of TPMS signals • Performing field test setup on the test track • Processing collected data The operation of TPMS sensors is presented in [8]. However, we performed further investigations and gained additional information about various others TPMS sensors. Our investigations are based on reverse engineering of the sensors operation. The procedure consisted of recording raw signals transmitted by the sensors followed by post processing using DSP approach. The raw signal is first demodulated and the bits were extracted. Next, the utilized encoding scheme was determined. Since the ID of the sensor was known, the first step was to find ID field inside the frame. After extracting ID the byte boundaries are set and the rest of the bytes were extracted. The positions of other fields, such as, pressure, temperature and error correction code were extracted using brute force method. As a result the frame formats of seven different sensor types from three most dominant vendors on the market have been obtained. The sensor types are particularly selected for covering a significant number of vehicle models from different car manufacturers. Based on the analysis of the sensor’s operation, the prototype receiver of TPMS signals was designed. One of the main challenges here was performing simultaneous reception of signals from the different TPMS sensors. Rouf et.al [12] have shown that TPMS signals could be received from a static sensor at the distance of approximately 40 m, but also from the sensor in motion at the speed of 35 km/h. For designing the prototype, the software defined radio approach was chosen, meaning that high frequency radio signal is down converted to baseband in hardware and after digitization sent to general purpose computer, where demodulation, decoding, checking and extracting of the data are accomplished. This approach provides high flexibility and possibility to implement parallel processing chains for different sensor types without multiplication of hardware. For this purpose, USRP device from Ettus Research [14] was used as radio frontend, while the software is based on GNU Radio framework [15]. The block diagram of the receiver is shown in Fig. 3. As can be seen, there are two main processing paths based on modulation type. One processing path is used for sensors which utilize ASK modulation, while the other is used for FSK modulation. Our receiver is capable of receiving signals from seven different types of TPMS sensors and measured range is around 100 m when directional antenna is used. Upon correct signal reception, receiver ID, sensor ID, sensor type and time stamp are stored in a database for post-processing. The experiment for collecting TPMS frames from the traffic was conducted on the DLR test track in Berlin, called UTRaLab. The UTRaLab (Urban Traffic Research Laboratory) is located at Ernst-Ruska- Ufer, Berlin, Germany (Fig. 4). It has a length of about 1 km and is equipped with two gantries that have a distance of 850 m. These gantries provide sensors for overhead detection, e.g. cameras, laser scanners, and sensors for wireless communication technologies, i.e. Bluetooth, WiFi and TPMS detectors. The loops are placed before and after each intersection to guarantee reliable measurements. Furthermore, the environmental sensors, e.g. visual-range meters, weather stations, ground sensor are also installed. The experiment consists of two prototype receivers placed on two gantries as shown in Fig. 4. In this setup we used directional Yagi antennas with the gain of 9 dBi. The antennas point towards the intersections in order to raise the number of detected sensor, due to the fact that many vehicles are forced to stop there. The intersections are located west of the west-bridge and east of the east-bridge, respectively, as in Fig. 4. TPMS data was collected over the course of three months, from February until May, 2014. The data collected at the test track need to be processed in order to derive traffic information, such as for example travel time. The main issue in processing TPMS data is dealing with data redundancy. Namely, the data redundancy exists due to the fact that every vehicle is usually equipped with four TPMS sensors. This implies that the number of detected sensors can be up to four times higher than the actual number of the vehicles. We assume that such redundancy would not introduce a significant error if, for example, only travel times are required. For extracting precise OD and routes information, the tracking of the sensors is not enough, and the tracking of vehicle is required. For this purpose an algorithm able to discover a group of sensors which belong to the same vehicle was designed. This allows us to couple sensor information to the vehicle information and enables vehicle identification. The algorithm is based on calculating probabilities of detecting different sensors of the same type at the same time on the same place. The procedure of processing the data consists of the following stages: grouping sensors from the same vehicle, assigning the group to a generated virtual vehicle ID, and, finally, deriving traffic information based on the information about detected vehicles. In the following section, the results of our experiment will be presented. In our experiment, over the period of three months, around 11000 unique sensors IDs were collected. The main drawback in the evaluating obtained results is inability to estimate the total number of vehicles equipped with TPMS on the test track. As a consequence, we are not able to perform precise estimation of the efficiency of our receiver. One of the first things we realized was the influence of sensor type on detectability of the sensor. The sensors can differ with respect to various parameters, but it is assumed that most dominant factors with respect to detection probability are: radiated power and message transmission period. Radiated power directly influences detection range, and test reports from different sensor types suggest that they radiate almost the same power. Transmission period specify how often sensors transmit their signal. The probability of signal reception rises with decreasing the value of transmission period. The contribution of every sensor type in the total amount of detected unique sensor IDs is shown in Table 1. Additionally, the available information about the number of models where sensor type was utilized, modulation, transmission period and radiated power is given. For confidentiality, sensor types are given generic names (A-G). As can be seen from Table 1, sensor type A has the lowest transmission period and this type was 47 % of all detections, which shows very widespread use of this type. The capability of detecting types C and D is added later in the experiment to the receiver’s chain and therefore the detection percentage is not giving the realistic figure of their application. If we take types A and B for example, they have been both in use since 2006 and they were utilized in 14 and 7 car models, respectively. The number of models implies that there should be twice as much vehicles equipped with type A than with type B. As ...

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Citations

... accidents and near-misses Saul et al., 2014) ). Furthermore, the development and test of wireless communication technologies for journey time measurements (like Wi-Fi, TPMS and Bluetooth sni ers) can be and has been be performed at the UTRaLab ( Savić et al., 2014)). Moreover, the UTRaLab was used as carrier and test platform to compare industrial and particularly developed optical tra c sensors in long term measurements (Reulke et al., 2008). ...
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The Urban Traffic Research Laboratory (UTRaLab) is a research and test track for traffic detection methods and sensors. It is located at the Ernst-Ruska-Ufer, in the southeast of the city of Berlin (Germany). The UTRaLab covers 1 km of a highly-frequented urban road and is connected to a motorway. It is equipped with two gantries with distance of 850 m in between and has several outstations for data collection. The gantries contain many different traffic sensors like inductive loops, cameras, lasers or wireless sensors for traffic data acquisition. Additionally a weather station records environmental data. The UTRaLab’s main purposes are the data collection of traffic data on the one hand and testing newly developed sensors on the other hand.
... Finally, we were able to derive travel times using TPMS. The results are presented in [20]. ...
... For traffic density above 80 veh/km, and speed less than 50 km/h, more than 90 % of the vehicles are detected. In one of our experiments that we conducted on the test track, sensors with a 5 s transmission period were the majority of the detected sensors, which is in accordance with the simulation results [20]. ...
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Full-text available
In this paper, we evaluate Tire Pressure Monitoring System (TPMS) for traffic management purposes. It has been shown that up to 60% of the vehicles can be detected in urban traffic environments, which makes it suitable for deriving: routes, travel times and the traffic state. In particular, the theoretical background and basic concepts are given. Furthermore, we present a simple simulation model of TPMS based on empirical investigations. A simulation platform, based on traffic simulator, used for evaluation is introduced. Next, simulation results related to the number of detected vehicles are given regarding detection range, sensor transmission period and traffic flow. The impact of the roadside unit's location, as well as the number of detected vehicles, is investigated by simulating a realistic traffic scenario. Finally, the applicability of TPMS for deriving different traffic information is evaluated.