Mobile-based telematics currently is one of the most rapidly developing fields. Utilizing smartphones for telematics entails various clear advantages, including the elimination of hardware costs. Since smartphones are becoming just natural, given that just almost everyone uses them, mobile-based telematics solutions could be fairly easily and effectively implemented.

Here we briefly describe how the power of mobile telematics could potentially be harnessed through the embedded modern smartphone sensors.

Sensors and functions

Modern smartphones equipped with efficient multitasking operating systems, rich set of embedded sensors, advanced microprocessors, and wireless / wired communication technology are largely diffused worldwide. Due to the ever-growing worldwide smartphone spread, the vehicle telematics industry obtained new pathways to collect data, which in turn has come to benefit fleet owners, drivers, businesses, and a whole society.

Embedded smartphone sensors are among the crucial components, providing mobile-based telematics  functionality progression. For sure, the actual sensors set largely depends on the particular smartphone model, but some of them already became very frequently used:

  • GPS unit
  • Gyroscope
  • Accelerometer
  • Magnetometer
  • Proximity sensor

GPS units within the smartphones get a ping from a satellite to figure out which part of the planet user standing on or driving through. GPS isn’t the only way a smartphone can work out where it is—distance to cell towers can also be used as a rough approximation. Modern GPS units inside smartphones could combine GPS signals with other datasets, for instance, cell signal strength, to estimate location more precisely.

Accelerometer detects acceleration, vibration, and tilt to determine movement and exact orientation along the three axes. Apps could use this smartphone sensor to determine whether it is in portrait or landscape orientation. It can also determine if the phone screen is facing upward or downward. The accelerometer can detect how fast the smartphone moves in any linear direction, and itself made up of other sensors, such as a microscopic crystal structure that becomes stressed under the accelerative forces. The working principle is based on MEMS(Micro Electro Mechanical System), which senses/measures the acceleration forces that may be caused by the movement or tilting action.

The gyroscope on its turn assists the accelerometer in figuring out which actual way smartphone is orientated, therefore adding a lot to precision. Gyroscope is especially useful in detecting small turns accurately enough: it can tell how much a smartphone has been rotated and in which direction. Similar to accelerometers, modern smartphone gyroscopes are also normally based on MEMS (Micro-Electro-Mechanical Systems).

Magnetometer measures magnetic fields and can determine which way is north by varying its voltage output to the phone. It operates symbiotically with the data originating from GPS unit and accelerometer, which helps to figure out the location and moving direction.

Proximity sensor is essentially a combination of  infra-red LED that sends out light pulses, a light detector that picks up the reflected pulses, and electronic circuitry that accurately measures the time difference between the emission of a pulse and the detection of its reflection.

Most commonly it is used to turn off the touch screen, while making a call with a smartphone positioned close to the ear. Similar types of sensors utilized in industry to sense the presence of objects or materials and then either initiate some action/flag their presence or absence.

Vehicle telematics using a smartphone

Due to the continuously growing worldwide smartphone connectivity, the vehicle, and telematics industry has generated new opportunities for data collection, which in turn has come to benefit fleet owners, drivers, and society as a whole.

Smartphone-based solutions are relatively cheap, scalable and upgradeable. In addition, smartphones could act as a natural platform for providing instantaneous driver feedback, enabling smooth integration of telematics services with existing networks.

As well, the replacement and development cycles of smartphones are usually noticeably shorter compare to those of vehicles, which makes them a favorable option for testing new technologies.

The information flow process of smartphone-based vehicle telematics is shown in the figure below. Measurements can be collected from both built-in smartphone sensors and from external, complementary sensor systems.

After sensing and processing measurements, data is communicated from the smartphone to a central data storage facility. Vehicle data aggregated at a central point can be used in e.g., traffic state estimation, traffic planning, or comparative driver analysis. Relevant information is sent back to the individual user.

The number of computations that are performed directly in the smartphone and at the central storage unit depend on the particular case, requested driver feedback and the interests of the service provider.

Telematics is often characterized by the inclusion of a feedback loop that enables a sensor-equipped end-user to control or change his behavior based on the results of the data analysis. In smartphone-based vehicle telematics, this could be exemplified by a driver who transmits driving data and then receives feedback that can be used to improve the safety or fuel-efficiency.

Smartphone-based automotive navigation is constrained by the fact that the sensor measurements depend not only on the vehicle dynamics, but also on the orientation, position, and movements of the smartphone relative to the vehicle. The three coordinate frames of interest in smartphone-based automotive navigation and methods that can be applied to infer their relationships are shown in the figure below.

Once the smartphone-to-vehicle orientation has been estimated, the IMU ( Inertial Measurement Unit) measurements can be rotated to the vehicle frame, allowing to estimate the acceleration in the vehicle’s forward direction directly from the IMU measurements.

Characterization of driving behavior/efficiency using motion sensors has become a rapidly evolving field. Before, this characterization used to be done with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals.

Example: Driver Behavior analysis Using Smartphone Sensors

Here we further consider an example, given in [Сonstandinos X. Mavromoustakis et al., 2017]. A usage-based auto-insurance information system, which consists of two elements, has been implemented. The first element is an android-based application for smartphones and tablets, which detects the driver’s behavior by analyzing the collected data from the device’s sensors. The second element is an e-platform, where someone can have access to all data (trip’s information, routes of trips, graphs of the sensor’s data) of all drivers, of the insurance company.

To get accurate measurements from a smartphone to evaluate the driving quality, it is required at first to calibrate the position of the device relative to that of the vehicle. This means virtually rotating the three-axis of the accelerometer sensor of the smartphone to meet the vehicle’s orientation. To do this one needs to know all the angles of rotation. To calculate the angles of rotation, a special function was utilized.

Following calibration, the authors combine data from three embedded smartphone sensors: the accelerometer, the magnetometer, and the gyroscope, in order to get the three orientation angles. The low noise gyroscope data is used only for orientation changes in short time intervals. The accelerometer and magnetometer data are used to support information over long time intervals.

The algorithm being used characterizes the behavior of the driver as Excellent, Very Good, Good, Bad or Very Bad and computes the average speed of the vehicle at the end of every trip.

The author’s research briefly mentioned above shows that the best practice for recognizing driving patterns is the use of accelerometer data. According to the authors, the reason is not that sensor fusion methodology is not accurate or reliable but rather that sensor fusion is not the best option for the detection of sharp turns or sharp lane changes.

Smartphone-based Vehicle Telematics: challenges

A noticeable amount of the embedded smartphone sensors are often of arguable quality and have not been originally designed for vehicle telematics applications.

Therefore, the employed algorithms must necessarily take into account the imprecision of smartphone sensors. Another challenging aspect is that the smartphone cannot be easily highly precisely fixed with respect to the vehicle, which entails some errors in the interpretation of data from orientation-dependent sensors such as gyroscopes, accelerometers, and magnetometers.

Another issue is the battery lifetime and power drain. All these aspects making discussions around the robustness and efficiency of smartphone-based telematics remain open.

With further advanced algorithms development, sensors quality increase and appearance of new sensors types, some of the mentioned issues could be addressed more efficiently.

References

  • Constandinos X. Mavromoustakis et al., Advances in Mobile Cloud Computing and Big Data in the 5G Era. 2017.
  • Johan Wahlström et al., Smartphone-based Vehicle Telematics — A Ten-Year Anniversary. 2016.
  • Sara Hernández Sánchez et al., Estimating Vehicle Movement Direction from Smartphone
    Accelerometers Using Deep Neural Networks, 2018.
  • Jair Ferreira, Júnior et al., Driver behavior profiling: An investigation with different smartphone
    sensors and machine learning. 2017.
  • Marco Grossi. A sensor-centric survey on the development of smartphone measurement and sensing
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