EP4227917A1

EP4227917A1 - Integrated system and method of analyzing vehicle operating parameters

Info

Publication number: EP4227917A1
Application number: EP22170593.2A
Authority: EP
Inventors: Maciej Torz; Agnieszka Drzymala Fec; Piotr Cyplik; Michal Adamczak; Lukasz Nowak; Lukasz Nowakowski; Michal Galas; Adriana Tobola
Original assignee: Rentis SA
Current assignee: Rentis SA
Priority date: 2022-02-15
Filing date: 2022-04-28
Publication date: 2023-08-16
Also published as: PL440390A1

Abstract

The subject of the invention is an integrated system and method of analyzing vehicle operating parameters. The sensing device includes a 3-axis overload sensor and is also equipped with an internal flash memory and a real-time clock module RTC. The electronic system of the sensing device is placed in a sealed ABS casing, detachably and rigidly mounted in the vehicle, preferably on the chassis beam. vehicle.

Description

The subject of the invention is an integrated system and method of analyzing vehicle operating parameters. Specifically designed to analyze the driver's driving style and generate prompts to reduce fuel consumption, CO₂ emissions and improve road safety.
Safety systems, aimed at minimizing the number of road incidents and their negative effects, are installed in all types of road vehicles. In the state of the art, the following examples are known and recognized: ABS, ESP, pressure sensors or E-Call systems. Car manufacturers propose more and more advanced active and passive safety systems, e.g. lane assist, distance between vehicles, road sign recognition, emergency brake assist, active cruise control, etc.
The statistics of road events indicate that these systems do not measurably affect road safety and reduce the loss ratio. The reason is that the driver has the option to ignore the signals given by these systems and continue driving the vehicle in a way that increases the risk of an accident or collision. The lack of a real impact of intelligent safety systems on driver behavior is due to several reasons, including the possibility of partial or complete deactivation of systems consciously allowed by manufacturers, which significantly reduces their impact on drivers, the fact that by ignoring warnings sent by safety systems the driver does not bear any consequences, and does not receive any rewards for proper responses to the warnings, as well as identified technological limitations in the scope of the analyzed driving parameters and the frequency of measurements which are insufficient for a meaningful analysis.
Car telematics tools are becoming more and more common, both in the case of independent suppliers and solutions implemented directly by car manufacturers. The factory-applied solutions (including EDR systems) do not have the appropriate impact on the driver, which is their biggest disadvantage. On the other hand, telematics solutions available on the market allow for the collection of data from sensors installed in passenger cars, but do not allow for comprehensive data transmission from the analytical system to customers or are limited only to selected parameters. The existing systems, in most cases, do not have the functionality of feedback communication with the vehicle's driver or implement such features to a very limited extent.
The aim of the invention is to provide a comprehensive, integrated system that identifies the driving style of the person renting or using the car based on a significantly expanded catalog of driving parameters and measurement modes, but also gives tips to focus on eco and safety driving to reduce the number of motor damage and CO₂ emissions by reducing fuel consumption. This goal was achieved on the basis of the following elements:

a new type of sensors collecting data on driving parameters wider than before,
a system that analyzes the driving style of the driver, based on the collected driving parameters,
software allowing interaction with the driver - an analytical panel, a set of advice and interpretation of driving style for the ongoing transfer of analysis conclusions to the driver, as well as a system of incentives.

A number of entities conduct analyzes on the impact of telematic solutions on road safety, in particular in relation to transport fleets in terms of supporting the transport services provided (Department of Modern Technologies in Logistics, University of
ódź) or optimization of costs of transport activities and reduction of the harmful impact of transport on the environment ( TomTom Telematics).
There are also known solutions aimed at improving fleet management by monitoring routes together with fuel consumption and working time statistics (NaviExpert Telematics) and trials of programs encouraging drivers to drive economically in order to reduce the costs of motor insurance (PZU S.A., Link4, Hestia).
The priorities of such solutions are usually not to reduce the negative impact of car traffic on the natural environment and to actually reduce the number of road incidents. This is due to the lack of direct connection of the measurement results (and their interpretation) with the end user (driver).
Among the telematics systems, in the prior art, EP1805703A1 describes a solution for monitoring a shipment using telematics achievements based on several identified parameters, i.e. internal and external temperature, acceleration, weight, hatch/cover condition and combinations thereof.
Another solution, described in publication FR2956639A1 , discloses a device that allows the vehicle position and speed to be checked, as well as the instantaneous acceleration to be verified. It focuses on the control of a narrow range of vehicle data.
The patent specification JP4443924B2 , on the other hand, describes a device that serves as a relay between various telematics devices. The solution cannot interpret the data. It is not intended to improve safety and reduce fuel consumption.
The publication KR20130139442A teaches of a system supporting so-called ecodriving, which is equipped with a unit collecting information about driving and the vehicle itself; a unit that extracts information about the road traveled and the driving sections, and a unit that allows the determination of eco-driving on the basis of data about the vehicle, its driving and the road traveled. The solution does not allow for broader analytics, in particular in the scope enabling the implementation of an incentive program or solutions in the field of the so-called defensive driving.
Another Korean publication KR20120061697A presents a tool that collects data on the driving behavior of the driver and suggests in the car how to improve the driving style to be eco-friendly (lower fuel consumption). However, the solution does not provide tools in the area of security.
In the prior art, from the patent description US9707973B2 , there is also known a solution enabling the identification of undesirable and risky driver behaviors and their analysis and reaction on the basis of two parameters, i.e. speed control and data from the lane keeping assistant system.
Yet another US description US20120109447A1 discloses a recording device that serves to recreate an accident after it has occurred "black-box". Records accident data. The device does not prevent accidents and has no effect on eco-driving.
US20060212195A1 discloses data recorder which collects various vehicle and driving performance parameters, among others. kilometers traveled, speed, acceleration / deceleration, brake activation, seat belt use, vehicle direction, steering anomalies, GPS position, impact forces and direction, transmission condition and alcohol use. This solution provides that the collected data can be reused by various cooperating systems, including, for example, intelligent ignition preventing drunk driver, real-time GPS positioning, jamming a cell phone with low power consumption and the possibility of internal wireless communication.
The solution closest to the invention is the telematics device presented in WO2015110857A1 , the operation of which is based on standard data from CAN and LIN modules, matching with GPS data, which data is transferred to external servers analyzing the data via the GSM network. However, this solution does not implement the features of the "black box", neither it does allow to reproduce the course of the road accident/incident on the basis of a data set in a continuous mode in a 15-second time loop with a frequency of not less than 60 times/s - as it is done by the invention.
The integrated system for analyzing vehicle performance parameters, in particular the driver's driving style, generating prompts to reduce fuel consumption, organically reduce CO₂ emissions and improve road safety, consists of three main elements:

sensor permanently installed in the car - the sensor must be rigidly connected to the car in order to correctly register the forces acting on the vehicle and be connected to the CAN bus of the car;
analytical system - responsible for the analysis of data collected by the sensor, the interpretation of the driver's driving style and the selection of tips (based on the driving style) directing drivers to a more ecological driving style,
mobile application - allowing to provide the driver with the results of the driving style analysis, tips and to participate in gamification with other car rentals.

The essence of the invention is an integrated system for analyzing vehicle operating parameters, consisting of a sensor device communicating with a remote computing unit, which unit enables feedback with the mobile device of the vehicle's user.
The sensor device with a control microprocessor contains GPS and GSM modules, the input block with a CAN module. It is characterized by that it also includes a 3-axis overload sensor in the sensor system. Moreover, it is equipped with an internal flash memory and a real time clock RTC module. The size of the flash memory should allow to save a minimum of 10,000 data frames - ie the set of information from the sensory system of the device. Moreover, the electronic system is housed in a sealed ABS housing - detachably and rigidly mounted in the vehicle, preferably mounted on the beam of the vehicle chassis.
Prefferably, the sensor device input block has a 1-Wire input and an input for third party devices using RS232, RS485 protocols, and the CAN module supports J1939, J1708, J1587 or FMS protocols.
The invention also relates to a method of analyzing vehicle performance parameters using the integrated vehicle performance analysis system described above.
According to the method, the sensing device reads the data from the sensors at a frequency of not less than 1 time per second, preferably every 16 ms, as a result of which a data frame is created containing:

sensor ID,
date and time,
longitude, latitude,
overloads in the x, y, z axes,
vector sum of overloads of the x, y, z axes,
vehicle speed,
pressure on the gas pedal,
RPM,
current gear.

Then, the control microprocessor compares the data from the data frame with the given speed intervals and speed maintaining time and recognizes the urban, non-urban, highway driving mode or event in course of driving and modifies the appropriate record of the next frame.
The control microprocessor then compresses the frame data and sends the compressed data to the database server of the remote computing unit at a defined frequency and receives an acknowledgment of receipt of the frame from the server.
If it is not possible to send data from the sensing device to the remote computing unit server due to loss of communication with the remote computing unit server, it writes the frames to the flash memory, and when the frame transmission is resumed, it sends the saved frames to the server and receives the confirmation of receiving the frame from the server.
The database server supports the analytical system that evaluates the individual driving parameters and user behavior, and counts the driver's driving style for the total assessment.
Then the results of the user's driving style assessment are made available to the user in the mobile application in the form of analytical messages, single driving assessment and summary user assessment, fuel consumption, CO₂ emissions, or messages of incentives or gamification.
Very preferably, when the control microprocessor of the sensor device identifies the case of exceeding the overload defined for each vehicle axle and / or exceeding the defined vector sum of overloads for each of the axes, it saves the data from the data frame to the flash memory in a time loop with a minimum length of 10 seconds before the event and 10 seconds after the congestion or sum of congestion events, and then sends the saved frames to the server when the transmission is resumed.
Examples of the system implementation and the method are shown by means of drawings, in which Fig. 1 shows a schematic view of the structure of the developed system, and Fig. 2 shows a block diagram of the sensor device.
The integrated system for analyzing vehicle performance, generating prompts to reduce fuel consumption, emission of CO₂ and improve road safety, consists of three main components;

sensory device - installed in the car;
a remote computing unit with a database system supporting the analytical system operating in the computing cloud;
a mobile application installed on drivers' smartphones.

The examples below show the components of the system. The task of the sensing device is to obtain data about the vehicle's location, forces acting on the vehicle and a set of data from the CAN bus.
For the purposes of the described solution, it was assumed that the sensory device must be equipped with the following functional blocks:

Power module
Block of inputs and outputs
SIM card slot
GPS / GSM antenna connector
GSM modem
GPS modem
CAN module
3D sensor
Flash memory
RTC module
Control microprocessor.

The size of the flash memory should allow to save a minimum of 10,000 data frames (currently 64Mb) - ie a set of information from the device's sensory system. Taking into account the currently available sensory data, the average size of a data frame is approximately 200 b (bytes). In individual cases, depending on the vehicle manufacturer or with the development of the sensors used, in particular those available via the CAN module, it is anticipated that the size of the data frame will increase.
The block diagram of the device is shown in Fig. 2
It was assumed that the sensory device meets the following technical and functional parameters:

Supply voltage from 8V to 32V
Power consumption:
60 mA operating mode,
30 mA standstill operation mode,
6 mA sleep mode.
ground responsive inputs.
analog inputs.
1-Wire input for drivers identification (Dallas, RFID).
Optional data reading from third devices using RS232 or RS485 protocols.
Reading data from the CAN bus using the J1939, J1708, J1587 or FMS protocols.
3-axis overload sensor (accelerometer).
GPS location.
Data transmission via GSM network.

In addition, it has a housing made of ABS - detachably and rigidly mounted in the vehicle - with catches on the beam of the vehicle chassis.
The sensor device, through the control microprocessor software, allows for the implementation of a number of functionalities important from the point of view of the method of analyzing the vehicle operation parameters:

Reading data from sensors with a frequency of not less than 1 time per second (currently the optimal solution is the frequency every 16 ms, with the use of software acceleration, a frequency of up to 200 times per second is expected)
Creating data frames
Transfer of data (data frames) to the server (with analytical system) with a defined frequency.

For the purposes of the blackbox function, the sensing device may also record data locally in a time loop with a minimum length of 10 seconds before the event and 10 seconds after the event. The event is understood as exceeding the defined overload for each axle of the vehicle and / or exceeding the defined vector sum of overloads for each of the axles. The sensing device allows the definition of overload thresholds for two states:

Engine on - events in motion / when parked with the engine on,
Engine off - the so-called parking events.

The sensory device continuously records data at a frequency of no more than 16 ms. If the sensor device is not able to send the time loop to the server, it saves the data in the device's flash memory,
The following data is recorded in the time loop data frame:

1. Sensor Id,
2. Date and time,
3. Longitude, latitude,
4. Overloads in the x, y, z axes,
5. Vector sum of overloads of the x, y, z axes,
6. Vehicle speed,
7. Pressure on the gas pedal
8. RPM
9. Current gear

For the purposes of the recognition of the driving mode, the sensing device identifies the driving mode: urban, non-urban or highway mode based on the set speed range and the time of its holding and data from the frames.
The sensory device has been designed in such a way as to collect as much data as possible about the car both while driving and when stationary. Thanks to the use of data from the CAN bus of the vehicle and its built-in sensors, such as a 3-axis overload sensor (gyroscope and accelerometer), it is possible to collect data allowing to determine the values of fourteen parameters that make up the driving style.
The selection of parameters, methods of determining their values and the scales for individual assessments are presented below. For a comprehensive identification of the driving style, the following parameters were used:

1. RPM;
2. Speed;
3. Pressure on the gas pedal;
4. Engine braking;
5. Cruise control;
6. Smooth driving;
7. Aggressive cornering;
8. Right run;
9. Rapid braking;
10. Rapid acceleration;
11. Kick Down;
12. Over speeding;
13. Idle gear;
14. Stops with the engine running.

For the purpose of identifying driving at a constant speed, the microprocessor controlling the sensing device recognizes on the basis of the defined permissible change in speed and the minimum duration of driving with a defined permissible change in speed and indicates the distances covered at a constant speed - virtual cruise control.
For the purposes of identifying the smoothness of driving, the microprocessor controlling the sensory device recognizes the events of smooth driving according to the scheme indicated below, which allows for defining at least 5 sharply closed speed ranges, for which the minimum time of the event is determined, where the event is taking the leg off the gas and pressing the gas pedal again. or taking the foot off the gas and applying the service brake, and sometimes the event is the time that elapses from taking the foot off the gas to depressing the accelerator again or taking the foot off the accelerator to depressing the service brake pedal.
The fulfillment of the conditions in the given configuration causes adding the value 1 to the smooth running event counter and sending a new event counter value in the nearest data frame.
In another example, for the purpose of identifying the gear in which the vehicle is running, the microprocessor controlling the sensing device on the basis of the current driving speed and the rotational speed of the engine identifies the gear in which the car is currently running. For technical reasons, this is not possible with continuously variable transmissions.
For the purposes of identifying sudden accelerations, sudden braking, too dynamically taken turns, the microprocessor controlling the sensing device has the ability to define limit values identifying rapid acceleration, braking, too dynamically covered a turn. An additional speed filter can also be used to identify too dynamically taken turns. The purpose of this filter is to separate out too dynamic curve events for speeds less than X km / h. The purpose of this filter is to exclude events from tight curves (e.g. on flyovers) traveling at a speed limit which is usually not less than 30 km / h).
The control microprocessor of the sensory device allows for the use of a data compression model before sending it to the database server, which model allows to limit the amount of data transferred to 20 MB per month, assuming that the vehicle travels an average distance of up to 5000 km per month.
The control microprocessor of the sensing device, when sending the frames to a remote server, performs the function of confirming the frames sent to the server.
The sensory device has the function of saving frames in non-volatile flash memory in case of loss of GSM coverage. The sensory device can store a minimum of 10,000 frames, thanks to the appropriate size of flash memory (currently the average "weight" of a frame is 200 bytes - therefore it is expected that the minimum memory can be 64Mb)
Moreover, it is envisaged that the sensor device enables the function of defining allowed networks (operators) in the case of data roaming or a separate definition of data frequencies in the case of roaming.
Additionally, it is envisaged that the sensor device can execute the function of sending an alarm in the case of:

Disconnection from the main power supply,
Disconnection of the GPS antenna,
Activation of the GPS jamming device.

Due to the fact that not all the parameters used to assess the driver's driving style can be read directly by the device, some of them must have been identified as an event by the microprocessor controlling the sensory device according to example findings:

Setting the driving mode

Setting the driving mode is to determine to which driving mode (urban / non-urban / highway) the currently read data set, sent in the data frame, should be assigned.
If the driving mode is identified, it is not possible to use the current position of the vehicle (built-up area, country road, expressway, etc.). This is due to the fact that a car standing in a traffic jam at highway gates definitely does not move in fast, but in urban mode. Similarly, a car cursing through city with an increased speed limit does not constitute an urban mode, but non-urban or highway mode.
Therefore, the following model was used for the purpose of assigning data sets to individual driving modes.
If, for the last X seconds, the vehicle speed has been:

1-60 km / h - give the data set the urban driving parameter.
61-100 km / h - give the data set the parameter extra-urban driving.
> 100 km / h, give the data set the parameter of driving (fast) on highways.

Both the time parameter [X] and the speed ranges are variables that can be remotely modified in the control microprocessor.

Establishing the smoothness of driving

Driving smoothness is a parameter by which the sensor device identifies whether the driver adjusts the speed to the current road conditions, and whether the distance to the vehicle in front is adjusted to both road conditions and speed.
The following basic assumption was made here: the driver, who adjusts the driving speed to the current road conditions and keeps a proper distance from the vehicles in front, as well as observes the traffic situation, performs the following sequence of events in due advance:

takes his foot off the gas and brakes the engine;
when braking with the engine, equalizes the speed of travel to the one with which the vehicle in front is moving;
gently (max 20% of the pressure on the brake pedal) brakes or presses the gas pedal again (eg when overtaking on the motorway).
takes his foot off the gas and brakes the engine;
gently brakes the vehicle and stops the vehicle (eg before an intersection).

Knowing the three sequences of successive events, the time that must elapse from taking the foot off the accelerator to depressing the accelerator pedal or gently depressing the brake pedal was determined.
The fulfillment of the above-mentioned time condition results in the passing of the smooth running parameter and sending this information in the data frame. This variable is sent in the form of an event counter, in which the total number of identified events from the time the sensor was installed until the next event occurs is sent.

Establishing a kick down

We understand kick down as a sudden pressure on the gas pedal. Any event in which the accelerator pedal is pressed> 80% in less than 1 second is considered a kick down.
This variable is sent in the form of an event counter in which the total number of identified events from the time of installation of the sensor device until the next event occurs is sent.

Establishing an aggressive turning

Determining an aggressive turning is based on the 3D sensor (accelerometer) readings. An aggressively completed curve event is identified when: at a speed> X km / h, the acceleration value in the transverse axis of the vehicle exceeds the value of Y.
The values of X, Y - like all other preset settings or intervals can be modified remotely within the sensor device.
The speed limit value was applied to avoid identifying the events of an aggressively defeated bend, e.g. when driving very tight descents / entrances at the permitted speed, e.g. on expressways.
This variable is sent in the form of an event counter in which the total number of identified events from the time of installation of the sensor device until the next event occurs is sent.

Establishing hard braking

Determining sudden braking is made based on the 3D sensor (accelerometer) readings. The sudden braking event is identified when: the acceleration value in the longitudinal axis of the vehicle will be lower than -X. The value of X can be modified remotely. This variable is sent in the form of an event counter in which the total number of identified events from the time of installation of the sensor device until the next event occurs is sent.

Establishing a rapid acceleration

The rapid acceleration is determined based on the 3D sensor (accelerometer) readings. The sudden acceleration event is identified when: the acceleration value in the longitudinal axis of the vehicle will be higher than X. The value of X can be modified remotely. This variable is sent in the form of an event counter, in which the total number of identified events from the installation of the sensor device until the next event occurs is sent.

Identification of the currently engaged gear

One of the assumed functions of the analytical system is the ability to analyze the distance traveled with the engine speed appropriate for a given gear. To meet this condition, it is necessary to read information about the gear currently used by the driver. On some vehicles, this information is available on the CAN bus. Not every vehicle can read the current gear from the CAN bus. For the purposes of identifying the current gear, the following model was adopted. (Of course, this identification only applies to traditional gearbox designs. This parameter will not be available for continuously variable transmissions.)
The current gear can be identified because the associated set of engine speed [RPM] and ground speed values is only possible for one specific gear.
Considering the above, for a given vehicle series, it is possible to set the following values:

Gear ratio on the main shaft of the gear.
Shift each gear forward.

On this basis, the final ratio of each gear can be determined. In addition to the gear ratio table of a given vehicle, the proper gear may also be determined by preset standard values for the dimensions of the car's driving wheels. This is necessary to calculate the circumference of the wheel. At this point, it should be noted that correct determinations are possible only when the car has standard wheels installed, i.e. those in which, despite the rim size change (standard in most vehicles), the manufacturer allows wheel sizes in which, despite the difference in the rim the circumference of the circle is constant. Maintaining a constant wheel circumference is necessary for the correct readings of the speedometer.
The circumference of the wheel is determined on the basis of the entered dimensions of the wheel. The example is shown below:
The standard size of the vehicle wheels has been determined:

16 inch rim diameter.
Tire profile width 205 mm.
Tire profile height 55% of the profile width.

Based on the above settings, it is determined:

$Rim diameter = 16 x 245 / 10 = 39.2 cm .$
$Total tire profile height = 2 x 205 * 0.55 = 22.55 cm .$
$Total wheel diameter = 0.392 + 0.2255 = 0.6175 m .$
$Wheel circumference = 0.6175 * 3.14 = 1.94 m .$

Based on a given driving speed and the corresponding engine speed, e.g .:

$V = 76 km / h = 21.11 m / s .$
RPM = 2800 rpm.

Then it is determined how many turns per second the wheel must make to travel the distance of 21.11 m. So 22.11 / 1.94 = 10.89 rev / sec.
Due to the fact that the rotational speed is given in rotations / minute, the above value changes to the rotations of the wheel per minute: 10.89 rpm ^∗ 60 = 653.26 rpm.
Knowing the engine rotations and the necessary number of wheel rotations to maintain a given speed, the necessary gear ratio between the engine and the wheel is determined to maintain the calculated wheel rotations at a given RPM. So we get: 2800 RPM / 653.26 RPM = 4.287.
It is then compared with the nearest value in the gear matrix corresponding to that established above. In this way, the 4th gear was identified.
After identifying the current gear, set the time during which the driver kept the right revs for the given gear and sends information in the data frame histogram about the distance covered in revolutions outside the optimal range.

Establishing the idle speed

Determining the currently engaged gear identifies only the forward gears. For the identification of idling, the following model has been implemented:
If V> 0 - RPM = RPM_bj = idling, where: RPM_bj - is idling rotation. The idling distance is sent in the histogram of the next data frame.

Determining a road incident in traffic

The establishment of a traffic incident is used to stop the black box time loop and to generate a traffic incident in a data frame. Identification of a road incident in traffic is carried out when:
The engine is running and

An overload greater than Xmax / Ymax / Zmax and / or will be recorded on the X / Y / Z axis of the vehicle
Vector sum of overloads from the X / Y / Z> S axes, where:
The S, Xmax / Ymax / Zmax values can be remotely configured.

At the time of recording a road event in motion, 10 seconds after this event, the recording of the black box module time loop is stopped, and the recorded data is sent in the form of a histogram in an additionally generated data frame.

Determining a traffic incident during a stoppage

Establishing a traffic incident while the vehicle is stationary is used to identify traffic incidents (parking jam) while the vehicle is stationary. It works according to the following scheme:
If the engine is stopped and

Overload on longitudinal> 0 or transverse> 0 or vertical <> 1 = traffic incident while stationary.

Information about such an event is sent in an additional data frame.

Defining driving at a constant speed (cruise control)

For cars not equipped with factory-made cruise control, it was necessary to implement the cruise control function in a sensor device that would identify the distances traveled with engine braking. This function is performed according to the rule: the driving speed does not change by more than +/- X km / h for a time longer than Y, count the distance traveled at a constant speed. Due to the fact that maintaining pressure on the gas pedal at a given speed in a given gear may be minimally variable, a tolerance parameter for this pressure has been introduced, for which the X value corresponds. X / Y values can be remotely configured. If a constant speed driving event is identified, the cruise control distance is calculated and the data is saved in the form of a histogram in the next data frame.

Setting engine braking

Engine braking distances are calculated when the following conditions are met:

Speed> 0 km / h i
Pressure on the accelerator pedal = 0%.

The fulfillment of this condition causes the calculation of the distance traveled with engine braking, which is saved in the form of a histogram in the next data frame.

Analytical system

The task of the analytical system is to collect data sent from a sensory device by means of a GSM module, their processing and distribution to a mobile application. A standard database server architecture, eg SQL, was adopted for the analytical system.
The analytical system must communicate with the mobile application, ensuring its access to data, for this purpose the API module of the mobile application is provided.
The database module for the API of the mobile application contains user data sets, aggregated results of individual drivers, their ratings, and position in rankings.
As part of the architecture, it is possible to use docker containerization, allowing for easy scaling of the solution in an environment such as Kuberenetes.
It is also planned to equip the analytical system with a CMS module, which will be the back office of the system administrator, allowing for the configuration of the mobile application, configuration of series of types, vehicles, and assessment intervals.
The panel also allows you to receive communication from the user, e.g. an overview of the inspections performed, gadgets purchased from the store, receipt of messages.
Each driver's journey is recorded in the database of the analytical system. If a ride takes place in different driving modes, it is divided into individual driving modes along with an indication of the distance of a given ride in a given driving mode.
For each ride, the distance of which was at least 2,000 meters (definable parameter), it is assessed and included in the overall assessment of the driver's driving style. Each parameter is assessed separately on the basis of the configuration made in the analytical system.

Stop with the engine running

This parameter defines what grade the driver will receive for the time spent with the engine running in each driving mode. The standstill time was determined in relation to the total engine operation time in a given drive, as shown in the example in Table 1.

Table1

Passenger car ON
Urban mode			Non-urban mode			Highway mode
Rate	% Total time		Rate	% Total time		Rate	% Total time
Rate	from	to	Rate	from	to	Rate	from	to
1	14	100	1	12	100	1	4	100
2	11	14	2	9	12	2	3	4
3	8	11	3	6	9	3	2	3
4	5	8	4	3	6	4	1	2
5	0	5	5	0	3	5	0	1

Similar parametric assumptions of the rating system are created for the other identified modes

Idling

This parameter defines what grade the driver will receive for idling. The evaluation takes into account the distance traveled by the driver at idle in a given drive in relation to the total distance of a given drive.

RPM- engine speed

This parameter defines what rating the driver will receive for driving in defined speed ranges. It should be noted here that these ranges are established separately for each vehicle series. The term vehicle type series is understood as a combination of variables - make / model / year of production / engine capacity / engine power / type of gearbox / type of fuel.

Speed

This parameter defines which speed rating the driver receives. In the example, it has been divided into 6 intervals to which grades have been assigned.

Pressure on the gas pedal

Within this parameter, for example, 6 pressure ranges for the accelerator pedal have been defined. For each of the ranges, it was determined what grade the driver would get from driving in a given range of pressure on the gas pedal.

Engine braking

Within this parameter, the example defines 6 intervals in which the ratio of the distance covered with engine braking to the total distance of the route was defined. A score has been assigned to each of the ranges.

Cruise control

It is a parameter that evaluates the distance traveled on the cruise control (at a constant speed) in relation to the total distance of the given ride. In the example, a grade has been assigned to the size.

Driving smoothness

This parameter counts the number of times the sensor device has registered the sequence of events that meets the smooth driving condition. The number of such events is related to the exemplary distance of 100 km. The rating is assigned to a certain number of events per 100 km.

Aggressive turns

Within this parameter, the recorded number of aggressively completed turns is counted for each 100 km of the route. The rating is assigned to a certain number of events per 100 km.

Rapid accelerations

This parameter specifies the recorded number of sharp accelerations for each 100 km of the route. The rating is assigned to a certain number of events per 100 km.

Sudden braking

Within this parameter, the recorded number of hard brakes is assessed for each 100 km of the route. The rating is assigned to a certain number of events per 100 km.

Kick down

Within this parameter, the recorded number of kick down events is assessed for every 100 km of the route. The rating is assigned to a certain number of events per 100 km.

Speeding

Within this parameter, the number of speeding is assessed for each 100 km of distance traveled. Speeding has been divided into 6 ranges, corresponding to the ranges used in the ticket tariff in the territory of the Republic of Poland, as below.

1.0-10 km / h
2.11-20 km / h
3.21-30 km / h
4.31-40 km / h
5. 41-50 km / h
6.> 50 km / h

The driver's score has been assigned to the defined number of events for a given interval. It is anticipated that the identification of the driving mode in the overspeed parameter may be defined based on the actual position of the vehicle. The permissible speed of travel in a given location can be read from the map server, e.g. Google Maps.

Right gear

Within this parameter, it is estimated what% of the distance the driver has traveled in the range of rotational speed appropriate for a given gear in relation to the total distance for a given ride. The rating is assigned to a specific distance.
The determined overall rating of the driving style results from the ratings given for each of the defined 14 driving parameters separately for each of the three driving modes: urban, extra-urban, and expressways. For each of the driving modes, the weights of the ratings of individual parameters in the total rating are defined separately. Rating weights can be changed over time, however, the change of rating weights is valid from the moment it is saved.
The solution proposed in the present invention allows for the analysis of as many as 14 driving parameters (stops on the engine on, engine braking, idling, EPM, vehicle speed, pressure on the gas pedal, driving at a constant speed, smooth driving, kick down). gas pedal, above 80% of its travel), properly selected gear ratio, aggressive cornering, rapid acceleration, sudden braking) and assumes the possibility of a global use of the mobile application, which will provide users with tips to change the driving style, and thus it goes about reducing the harmfulness of road transport on the environment and increasing safety.
It is anticipated that the user's application will enable the generation of various types of reports, e.g. ecodriving report from a single drive, which presents the evaluation of a given driver's eco-driving after the completion of the drive. Such a report will present the following data, broken down into urban, extra-urban, motorway and mixed driving, as well as eco-driving score, expressed with points on a scale of 0-5 and / or by means of agreed graphic messages and the assumed goal (minimum grade). Additionally, it will present the assessment of individual elements that make up the total assessment of the driving style.
The report may contain hints which elements of the driving technique should be changed by the driver in the first place, in order to improve the total score for the driving style in the shortest time, in accordance with the set weights.
Other possible reports are for example:
summary summary eco-driving summary (presents data aggregated from all trips made by the driver),

fuel consumption report - it presents aggregated data and data for individual journeys - it presents the expected value of fuel consumption for a given type of vehicle series (according to WLTP standards).
CO₂ emission report - presents aggregated data and data for individual journeys.
gamification reports - possible different programs of competition between drivers.

The report should contain hints which elements of the driving technique should be changed by the driver in the first place, in order to improve the total score for the driving style in the shortest possible time, in accordance with the set weights.
In addition, it is expected that the analysis of vehicle operating parameters will allow the use of an incentive system, the use of which results in an increase in the percentage of people following the application's prompts. Gamification that allows you to compare yourself with other drivers in various types of rankings is also an added value affecting the effectiveness of the tools proposed in this mobile application
It should be added here that the driving style is an individual factor, shaped by the training process during the acquisition of qualifications, driving experience, temperament, current psychophysical conditions, time pressure, force majeure factors and a number of other variables. It must be remembered that if you want to influence the driving style of the driver, you can actually only affect the acquired driver's qualities, without affecting incidental ones.
The above ensures that the driver is convinced that the real impact on changing the behavior of the driver can be achieved only through a comprehensive analysis of his behavior within the available technological solutions and presenting the results in terms that allow the driver to reflect in the context of:

costs related to the operation of the vehicle,
impact on your own safety and that of your relatives,
environmental impact.
incentive systems are an additional element motivating to work on changing the driver's habits.

It should be noted that the currently existing solutions for the assessment, analysis and impact on the driving style of the driver operate either to a limited extent (e.g. suggesting a gear change on the dashboard, economizers, etc.) and apply to a given vehicle model, or are based on sets parameters that do not allow to fully define the driving style of the driver, and thus have a real impact on its change.

Claims

Integrated system for analyzing vehicle operating parameters, consisting of a sensor device with a control microprocessor, including GPS and GSM modules, an input block with a CAN module, and a remote computing unit enabling feedback communication with the mobile device of the vehicle user, characterized in that it has a 3-axis overload sensor in the sensory system and moreover, it is equipped with an internal flash memory and a real-time clock module RTC, furthermore the electronic system of the sensing device is placed in a sealed ABS housing, detachably and rigidly mounted in the vehicle, preferably on the vehicle chassis beam.
The integrated system according to claim 1, characterized in that the sensor input block has a 1-Wire input, an input for third party devices using the RS232 or RS485 protocols, and the CAN module supports the J1939, J1708, J1587 or FMS protocols.
A method of analyzing vehicle performance parameters using the integrated system described in claim 1, characterized in that the sensor device reads data from the sensors at a frequency of not less than 1 time per second, preferably every 16 ms, as a result of which a data frame is created containing: sensor identifier, date and time, longitude, latitude, axis overloads x, y, z, vector sum of overloads of the x, y, z axes, vehicle speed, pressure on the gas pedal, RPM, current gear, then the control microprocessor compares the data from the data frame with the set speed ranges and its holding time and recognizes the urban driving mode, non-urban mode or highway mode or a driving event and modifies the appropriate record of the next frame, then the control microprocessor compresses the data of the frame and sends the compressed data to the database server of the remote computing unit with a defined frequency and receives the confirmation of the receipt of the frame from the server, and if it is not possible to send the data from the sensor device on the database server of the remote compute unit due to saves the frames to the flash memory after loss of communication, and if the frame transmission is resumed, it sends the saved frames to the server, while the database server operates the analytical system that evaluates the individual driving parameters and behavior of the user and counts the driving style for a total evaluation, then the results are shared are in the mobile application for the user in the form of analytical messages of the user's single and total driving assessment, fuel consumption, CO₂ emissions or messages of incentives or gamification.
The method of analyzing the usage parameters according to claim 3, characterized in that when it is impossible to send data from the sensor device to the server of the remote computing unit due to loss of communication with the remote server, the computing unit writes the frames to the flash memory, and in the case of obtaining the resumption of the possibility of transmission, the computing unit sends the frames saved frames to the server.
Method for analyzing operating parameters according to claim 3, characterized in that when the sensor device identifies the event of exceeding the overload defined for each vehicle axle or exceeding the defined vector sum of overloads for each of the axles, it saves the data from the data frame to the flash memory in a time loop. with a minimum length of 10 seconds before the event and 10 seconds after the overload or sum overload event, and then sends the saved frames to the server in the event of recovery of transmission capability.