EP3953736A1

EP3953736A1 - Optimal performance parameters of a vehicle

Info

Publication number: EP3953736A1
Application number: EP19718282.7A
Authority: EP
Inventors: Gerasimos OROLOGAS; Georgios SIKALIAS
Original assignee: Vehicle Performance Rating PC
Current assignee: Vehicle Performance Rating PC
Priority date: 2019-04-09
Filing date: 2019-04-09
Publication date: 2022-02-16
Also published as: WO2020207562A1

Abstract

Methods are provided for determining optimal performance parameters of a vehicle equipped with a GNSS device travelling or having travelled a route. These methods comprise:obtaining a sequence of GNSS-records produced by the GNSS device during the route, each of the GNSS-records including a position detected by the GNSS device and a time of detection of said position; splitting the sequence of GNSS-records into a plurality of sub-sequences of GNSS-records; calculating, for each of the sub-sequences, an acceleration depending on the positions and times of the GNSS-records; and determining a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations. Methods of determining an optimal time of completing a route by a vehicle and methods of determining performance of a driver driving a vehicle to complete a route are also provided. Systems, computer systems and computer programs suitable for performing said methods are also provided.

Description

OPTIMAL PERFORMANCE PARAMETERS OF A VEHICLE

The present disclosure relates to methods of determining one or more optimal performance parameters of a vehicle and to computer programs, systems and computing systems suitable for performing such methods.

BACKGROUND

Driving a vehicle, such as a car or a motorcycle, is a complicated skill. Driving a vehicle at its limit is an even more sophisticated challenge. Nowadays, in order to check how fast a vehicle can be driven, it is necessary to find and pay an available“professional” driver or rider, for him or her to drive the vehicle along a given route. The completion time by the professional may be taken as a reference time that may be compared with completion times of other drivers (being evaluated) for determining at which level of the vehicle’s limits said other drivers have driven the vehicle. The driving by the professional driver is thus taken as the maximum potential the vehicle can offer.

Every time a vehicle is to be driven, the conditions may be different. Different conditions of the car itself (e.g. wear tires), or different track surface conditions (e.g. wet, dry, damp), or even different temperature and humidity can affect dramatically the capabilities of the vehicle. So each time the vehicle is to be driven (in different conditions), a professional race driver-rider is required to estimate corresponding maximum potential of the vehicle as described in different parts of the disclosure.

Furthermore, the vehicle will never be in the same exactly condition as it was during the professional driver’s session. Tires, braking pads, weight and atmosphere conditions may almost always be different. Actually, nowadays it is almost impossible and very costly to determine to which extent a driver has taken advantage of the maximum potential of the vehicle, since environmental conditions and vehicle conditions are continuously changing, participation of a professional driver to estimate maximum potential of the vehicle may be very expensive, etc.

An object of the disclosure is to provide new tools such as methods, systems and computer programs aimed at improving current manners of estimating or determining maximum potential of a vehicle.

SUMMARY

In an aspect, a method is provided for determining one or more optimal performance parameters of a vehicle equipped with a GNSS device travelling or having travelled a route. The method comprises obtaining a sequence of GNSS-records produced by the GNSS device during the travelled route, each of the GNSS-records including a position detected by the GNSS device and a time of detection of said position. The sequence of GNSS-records is split into a plurality of sub-sequences of GNSS-records, and an acceleration (of the vehicle) is calculated for each of the sub-sequences depending on the positions and times of the GNSS-records in the sub-sequence. Finally, a maximum sustained acceleration is determined (as optimal performance parameter) by calculating an average value of a selection of higher calculated accelerations.

The route may be a predefined route or a non-predefined route. The GNSS device may be e.g. a Global Positioning System (GPS) device which, in some examples, may be a high precision GPS device. The vehicle may be further equipped with an IMU (inertial measurement unit) device. In this case, obtaining the sequence of GNSS-records may further include obtaining a sequence of IMU-records time-related with the GNSS- records. Each of the IMU-records may include inertial measurement(s) produced by the IMU device. Any of the calculations performed depending on positions and times in the GNSS-records may further depend on the inertial measurement(s) in IMU-records. The GNSS-records may be time-related with the IMU-records under a one-to-one relation such that one GNSS-record and one I MU-record are related to each other based on that the inertial measurement(s) in said IMU-record have been detected by the IMU device substantially at the time in said GNSS-record. Calculations based on measurements from GNSS device and IMU device may clearly improve accuracy of calculations. All these possible configurations may be generally referred to as Positioning System.

An expert or professional driver-rider can get the maximum of the vehicle in every bend, braking, acceleration, etc. on a lap or route at corresponding track, road. Non professional drivers-riders might achieve the same in some (few) points but not in all the way or route. A Positioning System can collect data regarding vehicle’s positions and times of detection of said positions (and inertial measurements if IMU device is used) in extreme details.

In every run and irrespective of driver’s skills, instant pick maximum acceleration-related values of the vehicle are calculated. These calculations are performed depending on positions and detection times of said positions from a Positioning System, and only those accelerations with highest values are selected. Different criteria may be used to perform said selection, such as e.g. selecting a percentage (e.g. 5%) of highest accelerations, a predefined number of highest accelerations, highest accelerations above a predefined acceleration threshold, etc. Once selected, said highest accelerations are averaged for obtaining“final” maximum acceleration-related values as optimal performance parameters of the vehicle.

The suggested method is thus based on averaging maximum acceleration values representing maximum pushing of the vehicle by the driver, whereas the other acceleration values representing non-maximum pushing of the vehicle are discarded. This permits obtaining maximum accelerations that the driver is able to get from the vehicle, which may correspond to maximum or almost maximum acceleration achievable in absolute terms, irrespective of whether the driver is a professional or a non professional driver.

These optimal performance parameters of the vehicle may be used to rank or rate e.g. different vehicles in view of their maximum potential, different drivers driving same or similar vehicle, different vehicle components (e.g. tires, brakes, shock absorbers, etc.), and so on.

In some examples, the above acceleration calculation may comprise calculating a longitudinal acceleration based on an evolution of speeds depending on the positions and times of the GNSS-records, and a lateral acceleration based on the evolution of speeds and a turn radius depending on the positions in the GNSS-records. A maximum sustained longitudinal deceleration may also be determined (as one of the optimal performance parameters) by calculating an average value of a selection of lower calculated longitudinal accelerations. A maximum sustained lateral acceleration may also be determined (as another one of the optimal performance parameters) by calculating an average value of a selection of higher calculated lateral accelerations. An acceleration-speed function may also be determined (as a further one of the optimal performance parameters), said function defining a maximum longitudinal acceleration depending on a speed at which said maximum longitudinal acceleration has occurred, depending on the positions and times in the GNSS-records.

Longitudinal accelerations above zero may be due to throttle operation, and longitudinal accelerations below zero or decelerations may be due to brake operation.

In some implementations, a method of determining an optimal time of completing a route by a vehicle may be provided. This “vehicle optimal time” method may comprise obtaining optimal performance parameters of the vehicle, including a maximum sustained lateral acceleration, a maximum sustained longitudinal deceleration, and an acceleration-speed function of the vehicle. These optimal performance parameters of the vehicle may be provided by a method of determining optimal performance parameters such as the ones described in different parts of the disclosure. The vehicle optimal time method may further include splitting the route formed as a series of route- positions into a plurality of sub-routes each formed as a sub-series of route-positions, and calculating, for each of the sub-routes, a turn radius depending on the route- positions forming the sub-route.

The vehicle optimal time method may still further include calculating, for each of the sub routes, an initial speed and a final speed of a current sub-route. The initial speed of the current sub-route may be equal to the final speed of a previous (adjacent) sub-route. The final speed of the current sub-route may be calculated depending on the initial speed and the turn radius of the current sub-route and the maximum sustained lateral acceleration and the acceleration-speed function of the vehicle.

The vehicle optimal time method may yet further comprise adjusting, for each of the sub routes, the initial speed of the current sub-route depending on the final speed and the turn radius of the current sub-route and the maximum sustained longitudinal deceleration of the vehicle. The adjusted initial speed may be assumed as the initial speed of the current sub-route.

The vehicle optimal time method may furthermore comprise calculating, for each of the sub-routes, an optimal time of completing the current sub-route by the vehicle depending on a length, the final speed and the initial speed of the current sub-route, along with obtaining the optimal time of completing the route by the vehicle based on summing all the sub-route optimal times.

According to examples, a method of determining performance of a driver driving a vehicle to complete a route may also be provided. Said“driver performance” method may comprise measuring a duration of completing the route by the driver driving the vehicle, and obtaining an optimal time provided by a method of determining an optimal time of completing a route by a vehicle such as the ones described in different parts of the disclosure. The driver performance method may further comprise determining the performance of the driver by comparing the measured duration of completing the route and the determined optimal time of completing the route.

In a further aspect, it is provided a system for determining one or more optimal performance parameters of a vehicle equipped with a GNSS device (generally called Positioning System herein) travelling or having travelled a route. This“vehicle optimal performance parameters” system comprises a GNSS module configured to obtain a sequence of GNSS-records produced by the GNSS device during the travelled route, each of the GNSS-records including a position detected by the GNSS device and a time of detection of said position.

The “vehicle optimal performance parameters” system further comprises a splitting module configured to split the sequence of GNSS-records into a plurality of sub sequences of GNSS-records.

The “vehicle optimal performance parameters” system still further comprises an acceleration module configured to calculate, for each of the sub-sequences, an acceleration depending on the positions and times of the GNSS-records in the sub sequence, and a max-sustained-acceleration module configured to determine a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations.

In a still further aspect, a computer program is provided comprising program instructions for causing a computing system to perform methods of determining one or more optimal performance parameters of a vehicle equipped with a Positioning System travelling or having travelled a route, such as the ones described in different parts of the disclosure.

In a still further aspect, a computing system is provided for determining one or more optimal performance parameters of a vehicle, the computing system comprising a memory and a processor, embodying instructions stored in the memory and executable by the processor, and the instructions comprising functionality or functionalities to execute methods of determining one or more optimal performance parameters of a vehicle, such as the ones described in other parts of the disclosure.

In some examples, it may be provided a vehicle, such as e.g. a car or a motorcycle, including any of the systems for determining one or more optimal performance parameters of the vehicle described in other parts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting examples of the disclosure will be described in the following, with reference to the appended drawings, in which:

Figure 1 is a block diagram schematically illustrating systems for determining one or more optimal performance parameters of a vehicle, according to examples; Figure 2 is a block diagram schematically illustrating systems for determining one or more optimal performance parameters of a vehicle, according to further examples; Figure 3 is a block diagram schematically illustrating systems for determining an optimal time of completing a route by a vehicle, according to examples;

Figure 4 is a block diagram schematically illustrating systems for determining performance of a driver driving a vehicle to complete a route, according to examples;

Figure 5 is a flow chart schematically illustrating methods of determining one or more optimal performance parameters of a vehicle, according to examples;

Figure 6 is a flow chart schematically illustrating methods of determining one or more optimal performance parameters of a vehicle, according to further examples;

Figure 7 is a flow chart schematically illustrating methods of determining an optimal time of completing a route by a vehicle, according to examples; Figure 8 is a flow chart schematically illustrating determination of a final speed of a vehicle in methods of determining an optimal time of completing a route by a vehicle, according to examples;

Figure 9 is a flow chart schematically illustrating adjustment of an initial speed of a vehicle in methods of determining an optimal time of completing a route by a vehicle, according to examples;

Figure 10 is a flow chart schematically illustrating methods of determining performance of a driver driving a vehicle to complete a route, according to examples;

Figure 11 a is a schematic illustration of splitting a route into sub-routes in methods according to previous figures or similar; and

Figure 11 b is a schematic illustration of calculating a turn radius of a sub-route in methods according to previous figures or similar.

DETAILED DESCRIPTION OF EXAMPLES

Figure 1 is a block diagram schematically illustrating systems for determining one or more optimal performance parameters of a vehicle equipped with a Positioning System travelling or having travelled a route, according to examples. These systems 100 may comprise a Positioning or GNSS module 101 , a splitting module 102, an acceleration module 103, and a max-sustained-acceleration module 103.

The GNSS module 101 may be configured to obtain or receive a sequence of GNSS- records 105 produced by the Positioning System during the travelled route, each of the GNSS-records 105 including a position detected by the Positioning System and a time of detection of said position. The splitting module 102 may be configured to split the sequence of GNSS-records 105 (from GNSS module 101 ) into a plurality of sub sequences of GNSS-records.

The Positioning System may further comprise an IMU device configured to produce inertial measurements. In this case, the GNSS module 101 may be further configured to obtain a sequence of IMU-records produced by the IMU device. IMU-records may be time-related with GNSS-records in same or similar manner as described in other parts of the disclosure (e.g. under one-to-one relation). The splitting module 102 may be further configured to split the sequence of IMU-records in same or similar manner as the splitting of the sequence of GNSS-records.

The acceleration module 103 may be configured to calculate, for each of the sub sequences of GNSS-records (from splitting module 102), an acceleration depending on the positions and times of the GNSS-records in the sub-sequence and, in some examples, previous sub-sequences thereto. The max-sustained-acceleration module 104 may be configured to determine a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations (from acceleration module 103).

The IMU-records may be used to calculate same or similar acceleration parameters as those calculated from the GNSS-records. Acceleration module 103 may be further configured to process IMU-records to perform such calculations. Acceleration parameters from GNSS-records and from IMU-records may be paired with each other for producing final acceleration parameters. For example, IMU based acceleration data and GNSS based acceleration data may be averaged to produce final acceleration parameters. Said combination of data/measurements from GNSS and IMU devices may improve accuracy of calculated acceleration data used to determine the optimal performance parameter(s) of the vehicle. Figure 2 is a block diagram schematically illustrating systems for determining one or more optimal performance parameters of a vehicle equipped with a Positioning System travelling or having travelled a route, according to further examples. Modules 201 and 202 of Figure 2 may be equal or similar to modules 101 and 102 of Figure 1. One difference between Figures 1 , 2 resides in that Figure 2 may include modules 203a and 203b configured to calculate lateral acceleration and longitudinal acceleration, respectively, which is not the case in Figure 1. Another difference resides in that Figure 2 may include modules 204a, 204b and 204c configured to calculate or determine maximum sustained lateral acceleration, maximum sustained longitudinal deceleration and acceleration-speed function, respectively, which is not the case in Figure 1.

The lateral acceleration module 203a may be configured to calculate, for each of the sub-sequences (from splitting module 202), a turn radius depending on the positions in the GNSS-records of the sub-sequence, and to calculate a lateral acceleration of the sub-sequence depending on the calculated turn radius (of the sub-sequence) and on an evolution of speeds depending on the positions and times of the GNSS-records in the sub-sequence and, in some examples, previous sub-sequences thereto.

The longitudinal acceleration module 203b may be configured to calculate, for each of the sub-sequences (from splitting module 202), a longitudinal acceleration based on the evolution of speeds.

The max sustained lateral acceleration module 204a may be configured to determine a maximum sustained lateral acceleration 207 by calculating an average value of a selection of higher calculated lateral accelerations (from the lateral acceleration module 203a).

The max sustained longitudinal deceleration module 204b may be configured to determine a maximum sustained longitudinal deceleration 208 by calculating an average value of a selection of lower calculated longitudinal accelerations (from the longitudinal acceleration module 203b).

The above average value of the selection of higher calculated lateral accelerations and the average value of the selection of lower calculated longitudinal accelerations may be calculated according to same or similar principles. In the one and/or the other case, calculating the average value of the selection may comprise identifying outliers in the selection whose discarding causes a standard deviation to be below a predefined deviation value or threshold, and averaging the selection without said identified outliers. The acceleration-speed function module 204c may be configured to determine an acceleration-speed function 206 defining a maximum longitudinal acceleration depending on a speed at which said maximum longitudinal acceleration has occurred, depending on the positions and times in the GNSS-records. The determination of the acceleration-speed function may include mapping the calculated longitudinal accelerations with speeds depending on positions and times in the GNSS-records. Said mapping may be performed in such a way that if several longitudinal accelerations have been calculated as occurred at the time of a particular speed, only the highest of said several longitudinal accelerations is included in the mapping. Once the mapping has been completed, the acceleration-speed function may be determined depending on the mapping by e.g. applying a curve fitting method to said mapping between calculated longitudinal accelerations and speeds. Figure 3 is a block diagram schematically illustrating systems for determining an optimal time of completing a route by a vehicle depending on optimal performance parameters of the vehicle, according to examples. Such“vehicle optimal time” systems 300 may comprise an obtaining module 301 , a splitting module 302, a turn radius module 303, an initial and final speed module 304, an adjusting module 305, a sub-route optimal time module 306, and a route optimal time module 307.

The obtaining module 301 may be configured to obtain a maximum sustained lateral acceleration 309, a maximum sustained longitudinal deceleration 308, and an acceleration-speed function 310 of the vehicle determined by a system for determining optimal performance parameters of a vehicle such as the ones described with reference to Figure 2. The obtaining module 301 may be further configured to obtain the (travelled or predefined) route. Such an obtaining of these data may include e.g. retrieving all or part of the data from a data repository (e.g. database, filesystem, etc.), receiving all or part of the data through a communications line, etc.

The splitting module 302 may be configured to split the route, which is formed as a series of route-positions, into a plurality of sub-routes each formed as a sub-series of route-positions. The turn radius module 303 may be configured to calculate, for each of the sub-routes (from the splitting module 302), a turn radius of the sub-route depending on the route- positions forming the sub-route. The initial and final speed module 304 may be configured to calculate, for each of the sub-routes, an initial speed and a final speed of a current sub-route. The initial speed of the current sub-route may be determined equal to final speed of a previous sub-route. The final speed of the current sub-route may be calculated depending on the initial speed of the current sub-route (previously determined at module 304 itself), the turn radius of the current sub-route (from the turn radius module 303), and the maximum sustained lateral acceleration 309 and acceleration-speed function 310 of the vehicle (from the obtaining module 301 ).

The adjusting module 305 may be configured to adjust, for each of the sub-routes, the initial speed of the current sub-route (from the initial and final speed module 304) depending on the final speed of the current sub-route (from the initial and final speed module 304), the turn radius of the current sub-route (from the turn radius module 303), and the maximum sustained longitudinal deceleration 308 of the vehicle (from the obtaining module 301 ). Once said adjustment has been completed, the initial speed of the current sub-route may be updated with the adjusted initial speed.

The sub-route optimal time module 306 may be configured to calculate, for each of the sub-routes, an optimal time of completing the current sub-route by the vehicle depending on a length of the current sub-route, and on the final and initial speeds of the current sub-route (from the initial and final speed module 304 and adjusting module 305).

The route optimal time module 307 may be configured to obtain the optimal time of completing the route by the vehicle 308 based on summing all the sub-route optimal times (from the sub-route optimal time module 306).

Figure 4 is a block diagram schematically illustrating systems for determining performance of a driver driving a vehicle to complete a route, according to examples. Such “driver performance” systems 400 may comprise an obtaining module 401 , a measuring module 402, and a comparing module 403.

The obtaining module 401 may be configured to obtain an optimal time of the vehicle to complete the route 404, determined by a system for determining an optimal time of completing a route by a vehicle such as the ones described with reference to Figure 3. The measuring module 402 may be configured to obtain or measure duration of completing the route by the driver driving the vehicle. The comparing module 403 may be configured to determine the performance of the driver by comparing the measured (or obtained) duration of completing the route (from measuring module 402) and the determined optimal time of completing the route by the vehicle (from obtaining module 401 ).

As used herein, the term“module” may be understood to refer to software, firmware, hardware and/or various combinations thereof. It is noted that the modules are exemplary. The modules may be combined, integrated, separated, and/or duplicated to support various applications. Also, a function described herein as being performed at a particular module may be performed at one or more other modules and/or by one or more other devices instead of or in addition to the function performed at the described particular module.

Moreover, the modules may be implemented across multiple devices, associated or linked to corresponding (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400, and/or to other components that may be local or remote to one another. Additionally, the modules may be moved from one device and added to another device, and/or may be included in both devices, associated to corresponding (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400. Any software implementations may be tangibly embodied in one or more storage media, such as e.g. a memory device, a floppy disk, a compact disk (CD), a digital versatile disk (DVD), or other devices that may store computer code.

The (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 according to present disclosure may be implemented by computing means, electronic means or a combination thereof. The computing means may be a set of instructions (e.g. a computer program) and then the (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 may comprise a memory and a processor, embodying said set of instructions stored in the memory and executable by the processor. The instructions may comprise functionality or functionalities to execute corresponding methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 such as e.g. the ones described with reference to other figures. In case the (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 is/are implemented only by electronic means, a controller of the system may be, for example, a CPLD (Complex Programmable Logic Device), an FPGA (Field Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit).

In case the (computer) methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 is/are a combination of electronic and computing means, the computing means may be a set of instructions (e.g. a computer program) and the electronic means may be any electronic circuit capable of implementing corresponding method steps of the proposed methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400 such as e.g. the ones described with reference to other figures.

The computer program(s) may be embodied on a storage medium (for example, a CD- ROM, a DVD, a USB drive, a computer memory or a read-only memory) or carried on a carrier signal (for example, on an electrical or optical carrier signal).

The computer program(s) may be in the form of source code, object code, a code intermediate source and object code such as in partially compiled form, or in any other form suitable for use in implementing the methods of determining vehicle optimal performance parameters 100, 200 and/or vehicle optimal time 300 and/or driver performance 400, according to present disclosure. The carrier may be any entity or device capable of carrying the computer program(s).

For example, the carrier may comprise a storage medium, such as a ROM, for example a CD ROM or a semiconductor ROM, or a magnetic recording medium, for example a hard disk. Further, the carrier may be a transmissible carrier such as an electrical or optical signal, which may be conveyed via electrical or optical cable or by radio or other means.

When the computer program(s) is/are embodied in a signal that may be conveyed directly by a cable or other device or means, the carrier may be constituted by such cable or other device or means. Alternatively, the carrier may be an integrated circuit in which the computer program(s) is/are embedded, the integrated circuit being adapted for performing, or for use in the performance of, the proposed methods. Figure 5 is a flow chart schematically illustrating methods for determining one or more optimal performance parameters of a vehicle equipped with a Positioning System (e.g. a GNSS/IMU device) travelling or having travelled a route, according to examples. Such methods may be performable by/through systems similar to the ones described with reference to Figures 1 , 2. Hence, number references from Figures 1 , 2 may be reused in following description of Figure 5 for the sake of completeness.

Methods of determining optimal performance parameters of a vehicle may start when a starting condition is detected at e.g. start block 500. Said starting condition may include e.g. activation of “vehicle optimal performance” functionality, reception of a starting signal indicating a user request for determining vehicle optimal performance parameters, etc.

Such “vehicle optimal performance” methods (according to Figure 5) may further comprise (at e.g. block 501 ) obtaining a sequence of GNSS-records produced by the Positioning System during the travelled route. Each of the GNSS-records may include a position detected by the Positioning System and a time of detection of said position. This GNSS-based functionality may be performed by e.g. the GPS module 101 , 201 of Figures 1 , 2. Functional considerations described with respect to said modules 101 , 201 may thus be similarly attributable to block 501.

Vehicle optimal performance methods (according to Figure 5) may still further comprise splitting (at e.g. block 502) the sequence of GNSS-records into a plurality of sub sequences of GNSS-records. This splitting functionality may be performed by e.g. the splitting module 102, 202 of Figures 1 , 2. Functional considerations described with respect to said modules 102, 202 may thus be similarly attributable to block 502.

Vehicle optimal performance methods (according to Figure 5) may yet further comprise (at e.g. block 503) calculating, for each of the sub-sequences, an acceleration depending on the positions and times of the GNSS-records. This acceleration calculating functionality may be performed by e.g. the acceleration module 103, 203 of Figures 1 , 2. Functional considerations described with respect to said modules 103, 203 may thus be similarly attributable to block 503.

Vehicle optimal performance methods (according to Figure 5) may furthermore comprise determining (at e.g. block 504) a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations. This max-acceleration functionality may be performed by e.g. the max-sustained-acceleration module 104, 204 of Figures 1 , 2. Functional considerations described with respect to said modules 104, 204 may thus be similarly attributable to block 504.

Vehicle optimal performance methods (according to Figure 5) may still furthermore comprise (at e.g. block 505) finalizing the method by e.g. providing results of the execution of the method, releasing computing resources for subsequent executions of the method, etc.

Figure 6 is a flow chart schematically illustrating methods for determining one or more optimal performance parameters of a vehicle equipped with a Positioning System travelling or having travelled a route, according to further examples. Such methods are performable by/through systems similar to the ones described with reference to Figure 2. Hence, number references from Figure 2 may be reused in following description of Figure 6 for the sake of completeness.

Blocks 600 - 602, 608 of Figure 6 may be equal or similar to blocks 500 - 502, 505 of Figure 5, respectively. One difference between Figures 5 and 6 may reside in that “vehicle optimal performance” methods according to Figure 6 may include block 603 to calculate lateral acceleration and block 605 to calculate maximum sustained lateral acceleration, which are not present in methods according to Figure 5. Another difference may reside in that“vehicle optimal performance” methods according to Figure 6 may include block 604 to calculate longitudinal acceleration, block 606 to calculate maximum sustained longitudinal deceleration, and block 607 to determine acceleration-speed function, which are not present in methods according to Figure 5.

Vehicle optimal performance methods (according to Figure 6) may further include (at e.g. block 603) calculating, for each of the sub-sequences (from block 602), a lateral acceleration of the sub-sequence in same or similar manner as described with respect to the lateral acceleration module 203a of Figure 2. In fact, this functionality may be performed by said module 203a of Figure 2 and, in general, functional considerations described with respect to said module 203a may be similarly attributable to block 603.

Vehicle optimal performance methods (according to Figure 6) may further include (at e.g. block 604) calculating, for each of the sub-sequences (from block 602), a longitudinal acceleration of the sub-sequence in same or similar manner as described with respect to the longitudinal acceleration module 203b of Figure 2. In fact, this functionality may be performed by said module 203b of Figure 2 and, in general, functional considerations described with respect to said module 203b may be similarly attributable to block 604.

Vehicle optimal performance methods (according to Figure 6) may still further include (at e.g. block 605) calculating a maximum sustained lateral acceleration (depending on lateral accelerations from block 603) in same or similar manner as described with respect to the maximum sustained lateral acceleration module 204a of Figure 2. In fact, this functionality may be performed by said module 204a of Figure 2 and, in general, functional considerations described with respect to said module 204a may be similarly attributable to block 605.

Vehicle optimal performance methods (according to Figure 6) may yet further include (at e.g. block 606) calculating a maximum sustained longitudinal deceleration (depending on longitudinal accelerations from block 604) in same or similar manner as described with respect to the max sustained longitudinal deceleration module 204b of Figure 2. In fact, this functionality may be performed by said module 204b of Figure 2 and, in general, functional considerations described with respect to said module 204b may be similarly attributable to block 606.

Vehicle optimal performance methods (according to Figure 6) may furthermore include (at e.g. block 607) calculating an acceleration-speed function (depending on longitudinal accelerations from block 604) in same or similar manner as described with respect to the acceleration-speed function module 204c of Figure 2. In fact, this functionality may be performed by said module 204c of Figure 2 and, in general, functional considerations described with respect to said module 204c may be similarly attributable to block 607.

Figure 7 is a flow chart schematically illustrating methods of determining an optimal time of completing a route by a vehicle depending on optimal performance parameters of the vehicle, according to examples. Such methods may be performable by/through systems similar to the ones described with reference to Figure 3. Hence, number references from Figure 3 may be reused in following description of Figure 7 for the sake of completeness.

Methods of determining an optimal time of completing a route by a vehicle may start when a starting condition is detected at e.g. start block 700. Said starting condition may include e.g. activation of“vehicle optimal time” functionality, reception of a starting signal indicating a user request for determining vehicle optimal time, etc. Such“vehicle optimal time” methods (according to Figure 7) may further comprise (at e.g. block 701 ) obtaining optimal performance parameters of the vehicle (determined by methods according to e.g. Figure 6) in same or similar manner as described with respect to the obtaining module 301 of Figure 3. This obtaining of parameters (at e.g. block 701 ) may further include obtaining the (travelled or predefined) route. This “obtaining” functionality may thus be performed by said module 301 of Figure 3 and, in general, functional considerations described with respect to said module 301 may be similarly attributable to block 701.

Vehicle optimal time methods (according to Figure 7) may still further comprise (at e.g. block 702) splitting the route into sub-routes in same or similar manner as described with respect to the splitting module 302 of Figure 3. This splitting functionality may thus be performed by said module 302 of Figure 3 and, in general, functional considerations described with respect to said module 302 may be similarly attributable to block 702.

Vehicle optimal time methods (according to Figure 7) may yet further comprise (at e.g. block 703) calculating a turn radius for each sub-route (from block 702) in same or similar manner as described with respect to the turn radius module 303 of Figure 3. This functionality may hence be performed by said module 303 of Figure 3 and, in general, functional considerations described with respect to said module 303 may be similarly attributable to block 703.

Vehicle optimal time methods (according to Figure 7) may furthermore include (at e.g. block 704) calculating initial and final speed for each sub-route in same or similar manner as described with respect to the initial and final speed module 304 of Figure 3. This functionality may therefore be performed by said module 304 of Figure 3 and, in general, functional considerations described with respect to said module 304 may be similarly attributable to block 704.

Vehicle optimal time methods (according to Figure 7) may still furthermore include (at e.g. block 705) adjusting the initial speed for each sub-route in same or similar manner as described with respect to the adjusting module 305 of Figure 3. This functionality may thus be performed by said module 305 of Figure 3 and, in general, functional considerations described with respect to said module 305 may be similarly attributable to block 705.

Vehicle optimal time methods (according to Figure 7) may yet furthermore include (at e.g. block 706) calculating an optimal time for each sub-route in same or similar manner as described with respect to the sub-route optimal time module 306 of Figure 3. This functionality may thus be performed by said module 306 of Figure 3 and, in general, functional considerations described with respect to said module 306 may be similarly attributable to block 706.

Vehicle optimal time methods (according to Figure 7) may additionally include (at e.g. block 707) calculating an optimal time for the whole route 308 in same or similar manner as described with respect to the route optimal time module 307 of Figure 3. This functionality may thus be performed by said module 307 of Figure 3 and, in general, functional considerations described with respect to said module 307 may be similarly attributable to block 707.

Vehicle optimal time methods (according to Figure 7) may further additionally include (at e.g. block 708) finalizing the method by e.g. providing results of the execution of the method, releasing computing resources for subsequent executions of the method, etc.

Figure 8 is a flow chart schematically illustrating determination of a final speed of a vehicle in a current sub-route, according to examples. This final-speed determination is usable in methods of determining an optimal time of completing a route by a vehicle according to e.g. Figure 7. In particular, block 704 of said methods may include such a final-speed determination. Hence, number references from Figure 7 may be reused in following description of Figure 8 for the sake of completeness.

Such final-speed determinations according to Figure 8 may start (at e.g. starting block 800) when necessary data for determining the final speed is available within block 704 in methods according to Figure 7.

Final-speed determinations (according to Figure 8) may further comprise calculating (at e.g. block 801 ) a maximum final speed based on the initial speed of the current sub route (determined within block 704), and a maximum longitudinal acceleration outputted by the acceleration-speed function of the vehicle (from block 701 ) depending on the initial speed of the current sub-route (determined within block 704).

Final-speed determinations (according to Figure 8) may still further comprise calculating (at e.g. block 802) a required lateral acceleration of the current sub-route depending on the turn radius (from block 703) and the maximum final speed (from block 801 ) of the current sub-route. Final-speed determinations (according to Figure 8) may yet further comprise (at e.g. block 803) whether the required lateral acceleration of the current sub-route (from block 802) is above the maximum sustained lateral acceleration of the vehicle (from block 701 ). In case of affirmative or true result of said verification, a transition to block 805 may be performed to determine the final speed of the current sub-route depending on the turn radius of the current sub-route (from block 703) and the maximum sustained lateral acceleration of the vehicle (from block 701 ). Otherwise, a transition to block 804 may be performed to determine the final speed of the current sub-route equal to the maximum final speed of the current sub-route (from block 801 ). Once the final speed has been determined, a transition to end block 806 may be performed.

Figure 9 is a flow chart schematically illustrating adjustment of an initial speed of a vehicle in a current sub-route, according to examples. This initial-speed adjustment is usable in methods of determining an optimal time of completing a route by a vehicle according to e.g. Figure 7. In particular, block 705 of said methods may include such an initial-speed adjustment. Furthermore, said initial-speed adjustment may use data from final-speed determinations according to e.g. Figure 8. Hence, number references from Figures 7, 8 may be reused in following description of Figure 9 for the sake of completeness.

Such initial-speed adjustments according to Figure 9 may start (at e.g. starting block 900) when necessary data for adjusting the initial speed is available at block 705 in methods according to Figure 7.

Initial-speed adjustments (according to Figure 9) may further comprise calculating (at e.g. block 901 ) an estimated lateral acceleration of the current sub-route depending on the turn radius (from block 703) and the final speed (from block 806) of the current sub route.

Initial-speed adjustments (according to Figure 9) may still further comprise calculating (at e.g. block 902) a longitudinal deceleration of the current sub-route based on the estimated lateral acceleration of the current sub-route (from block 901 ), and on a maximum combined acceleration depending on the maximum sustained lateral acceleration (from block 701 ) and the maximum sustained longitudinal deceleration (from block 701 ) of the vehicle.

The maximum combined acceleration may be determined based on following formula: max _combined_accel = g max _lat_accel² + max Jong _decel² wherein max _combined_accel is the maximum combined acceleration, max Jat_accel is the maximum sustained lateral acceleration of the vehicle, and max Jong_decel is the maximum sustained longitudinal deceleration of the vehicle.

Initial-speed adjustments (according to Figure 9) may yet further comprise calculating (at e.g. block 903) an alternative initial speed depending on the final speed (from block 806) and the longitudinal deceleration (from block 902) of the current sub-route. Initial-speed adjustments (according to Figure 9) may furthermore comprise (at e.g. block 904) whether the initial speed of the current sub-route (from block 704) is above the alternative initial speed (from block 903). In case of affirmative or true result of said verification, a transition to block 906 may be performed to update the initial speed of the current sub-route (from block 704) with the alternative initial speed (from block 903). Otherwise, a transition to block 905 may be performed to keep the initial speed of the current sub-route (from block 704) unaltered. Once the initial speed of the current sub route has been adjusted, a transition to end block 907 may be performed.

Figure 10 is a flow chart schematically illustrating methods of determining performance of a driver driving a vehicle to complete a route, according to examples. Such methods may be performable by/through systems similar to the ones described with reference to Figure 4. Hence, number references from Figure 4 may be reused in following description of Figure 10 for the sake of completeness. Methods of determining performance of a driver may start when a starting condition is detected at e.g. start block 1000. Said starting condition may include e.g. activation of “driver performance determination” functionality, reception of a starting signal indicating a user request for determining performance of a driver, etc. Such“driver performance determination” methods (according to Figure 10) may further comprise (at e.g. block 1001 ) measuring or obtaining a duration of completing the route by the driver driving the vehicle in same or similar manner as described with respect to the measuring module 402 of Figure 4. This functionality may thus be performed by said module 402 and, in general, functional considerations described with respect to said module 402 may be similarly attributable to block 1001.

Driver performance determination methods (according to Figure 10) may still further comprise obtaining (at e.g. block 1002) an optimal time of completing the route by the vehicle 404 in same or similar manner as described with respect to the obtaining module 401 of Figure 4. This functionality may thus be performed by said module 401 and, in general, functional considerations described with respect to said module 401 may be similarly attributable to block 1002.

Driver performance determination methods (according to Figure 10) may yet further comprise determining (at e.g. block 1003) the performance of the driver 405 in same or similar manner as described with respect to the comparing module 403 of Figure 4. This functionality may thus be performed by said module 403 and, in general, functional considerations described with respect to said module 403 may be similarly attributable to block 1003.

Driver performance determination methods (according to Figure 10) may furthermore comprise (at e.g. block 1004) finalizing the method by e.g. providing results of the execution of the method, releasing computing resources for subsequent executions of the method, etc.

In the previous descriptions, many calculations and determinations have been cited as features that are known in the state of the art and, therefore, no specific details have been provided thereon. For example, it is known for a skilled person how speed, longitudinal acceleration and their evolution over time may be calculated from a sequence of GNSS position-time records and, in some examples, IMU inertial data records. It is also well-known how to calculate an average value, a turn radius from a sequence of positions, a lateral acceleration based on turn radius and speed, etc. Another example of pre-known concept and calculation thereof may be the length of segments (or sub-sequences or sub-routes) used in methods disclosed herein. Figures 1 1a and 1 1 b have been included herein to briefly describe two examples of such calculations as a mere sample of said known concepts and calculations thereof.

Figure 1 1a is a schematic illustration of splitting a route (or a sequence of GNSS positions) into sub-routes (or sub-sequences of GNSS positions) in methods according to previous figures or similar. As shown in the figure, positions in GNSS-records 1100 according to a reference coordinate system may be grouped in different segments (or sub-sequences or sub-routes) of consecutive positions between initial position 1102 and final position 1 101 defining the ends of the segment 1 103. Intermediate positions (not shown) may of course exist between initial and end positions 1 101 , 1102. Different criteria may be used to define such segments, such as e.g. a predefined segment length, a predefined number of GNSS positions between ends 1 101 , 1102 of the segment, a predefined time elapsed between ends 1101 , 1 102 of the segment, etc.

Figure 1 1 b is a schematic illustration of calculating a turn radius of a sub-route in methods according to previous figures or similar. This concept applies to segments (or sub-sequences or sub-routes) of consecutive positions defining a curved path (in opposition to a straight path). In the particular example shown, two curved segments are shown, one between end positions 1 107 - 1106 and the other between end positions 1106 - 1 105. A circular interpolation may be applied to said segments to find a circle a part of which substantially matches the curved path of the segment 1107 - 1106 or 1 106 - 1 105, and to finally determine a radius 1 104 of said matching circle as the turn radius of the segment 1107 - 1 106 or 1106 - 1 105.

Any of the described methods of determining vehicle optimal performance parameters may be (continuously or regularly or frequently) repeated according to predefined repetition criterion or criteria, so as to refresh the optimal performance parameter(s) of the vehicle. Such predefined repetition criterion or criteria may include e.g. a predefined elapsed time between a repetition and a next one, a predefined distance travelled by the vehicle between a repetition and a next one, initiating repetition when a predefined position in the route is reached, etc. or any combination thereof. In methods of determining an optimal time of completing a route by a vehicle, optimal performance parameters of the vehicle may be obtained from the last repetition of the method of determining optimal performance parameters of the vehicle.

Any of the previously described methods of determining vehicle optimal performance parameters and/or optimal time of completing a route may be used to compare maximum potential of different vehicles. This may be used to e.g. ranking or rating different vehicles and/or different vehicle components that may influence the performance of a vehicle, such as e.g. tires, brakes, shock absorbers, etc. In methods of determining vehicle optimal performance parameters, said parameters derived from different vehicles and/or different vehicle components may be compared to define corresponding ranking or rating of vehicles and/or vehicle components. Systems and methods performing such a rating of vehicle performance may be denominated as “Vehicle Performance Rating” systems and methods, respectively.

In methods of determining optimal time of completing a route by a vehicle, said optimal time derived from different vehicles and/or different vehicle components may be compared to define corresponding ranking of vehicles and/or vehicle components. Different drivers may be also ranked according to same or similar principles but, in this case, by using methods of determining performance of a driver driving a vehicle to complete a route. In any case, very interesting applications of the disclosed methods may exist in car or moto racing, in which vehicles, drivers and vehicle components are constantly monitored to maximize performance of the vehicle-driver binomial.

In any of the methods described herein, the vehicle travelling or having travelled the route may further comprise the aforementioned IMU device including some inertial sensor(s), such as e.g. accelerometer, gyroscope, etc. Methods according to present disclosure may use inertial data from the IMU device to e.g. verify and/or refine correctness of motion/acceleration data derived from GNSS-records. Data from the IMU device may also be used to generate motion/acceleration data to compensate “incalculable” motion data due to e.g. absence of GNSS-records at a given time or period of time during the route. Absence of GNSS-records may be due to e.g. temporary loss of satellite signal, malfunction of the GNSS, malfunction of communications with the GNSS or any other distortion that can occur in such systems.

Although only a number of examples have been disclosed herein, other alternatives, modifications, uses and/or equivalents thereof are possible. Furthermore, all possible combinations of the described examples are also covered. Thus, the scope of the disclosure should not be limited by particular examples, but should be determined only by a fair reading of the claims that follow.

Claims

1. A method of determining one or more optimal performance parameters of a vehicle equipped with a Global Navigation Satellite System (GNSS) device travelling or having travelled a route, the method comprising:

obtaining a sequence of GNSS-records produced by the GNSS device during the travelled route, each of the GNSS-records including a position detected by the GNSS device and a time of detection of said position;

splitting the sequence of GNSS-records into a plurality of sub-sequences of GNSS-records;

calculating, for each of the sub-sequences, an acceleration depending on the positions and times of the GNSS-records in the sub-sequence;

determining a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations.

2. A method according to claim 1 , wherein the method is repeated according to predefined repetition criterion or criteria, so as to refresh the one or more optimal performance parameters of the vehicle.

3. A method according to claim 2, wherein the predefined repetition criterion or criteria include at least one of among: a predefined elapsed time between a repetition and a next one, a predefined distance travelled by the vehicle between a repetition and a next one, initiating repetition when a predefined position in the route is reached.

4. A method according to any of claims 1 to 3, wherein calculating an acceleration for each of the sub-sequences comprises determining, for each of the sub-sequences and depending on the positions and times of the GNSS-records in the sub-sequence, at least one of among a lateral acceleration and a longitudinal acceleration; and wherein determining a maximum sustained acceleration for each of the sub-sequences comprises calculating, for each of the sub-sequences, at least one of among:

a maximum sustained lateral acceleration by calculating an average value of a selection of higher calculated lateral accelerations, a maximum sustained longitudinal deceleration by calculating an average value of a selection of lower calculated longitudinal accelerations, and an acceleration-speed function defining a maximum longitudinal acceleration depending on a speed at which said maximum longitudinal acceleration has occurred depending on the positions and times in the GNSS-records.

5. A method according to claim 4, wherein determining a lateral acceleration for each of the sub-sequences comprises:

calculating, for each of the sub-sequences, a turn radius depending on the positions in the GNSS-records of the sub-sequence;

calculating, for each of the sub-sequences, the lateral acceleration depending on the turn radius of the sub-sequence and an evolution of speeds depending on the positions and times of the GNSS-records in the sub-sequence.

6. A method according to any of claims 4 or 5, wherein calculating an average value of a selection of higher calculated lateral accelerations comprises:

identifying outliers in the selection of higher calculated lateral accelerations whose discarding causes a standard deviation to be below a predefined deviation threshold; averaging the selection of higher calculated lateral accelerations without said identified outliers.

7. A method according to any of claims 4 to 6, wherein determining a longitudinal acceleration for each of the sub-sequences comprises:

calculating, for each of the sub-sequences, the longitudinal acceleration based on an evolution of speeds depending on the positions and times of the GNSS-records in the sub-sequence.

8. A method according to any of claims 4 to 7, wherein calculating an average value of a selection of lower calculated longitudinal accelerations comprises:

identifying outliers in the selection of lower calculated longitudinal accelerations whose discarding causes a standard deviation to be below a predefined deviation value; averaging the selection of lower calculated longitudinal accelerations without said identified outliers.

9. A method according to any of claims 4 to 8, wherein determining an acceleration- speed function defining a maximum longitudinal acceleration depending on a speed at which said maximum longitudinal acceleration has occurred comprises:

mapping the calculated longitudinal accelerations with speeds depending on positions and times in the GNSS-records, wherein if several longitudinal accelerations have been calculated as occurred at the time of a particular speed, only a highest of said several longitudinal accelerations is included in the mapping;

determining the acceleration-speed function based on said mapping between the calculated longitudinal accelerations and speeds.

10. A method according to claim 9, wherein determining the acceleration-speed function based on said mapping comprises applying a curve fitting method to said mapping between calculated longitudinal accelerations and speeds.

1 1. A method according to any of claims 1 to 10, wherein the GNSS device is a Global Positioning System (GPS) device, preferably a high precision GPS device.

12. A method according to any of claims 1 to 11 , wherein the vehicle is further equipped with an Inertial Measurement Unit (IMU) device; wherein obtaining the sequence of GNSS-records further includes obtaining a sequence of IMU-records time- related with the GNSS-records, each of the IMU-records including one or more inertial measurements produced by the IMU device; and wherein said inertial measurements are also taken into account in calculations performed depending on the positions and times of the GNSS-records.

13. A method according to claim 12, wherein the IMU-records are time-related with the GNSS-records under a one-to-one relation, such that one GNSS-record and one IMU- record are related to each other based on that the one or more inertial measurements in said IMU-record have been detected by the IMU device substantially at the time in said GNSS-record.

14. A method of determining an optimal time of completing a route by a vehicle taking into account optimal performance parameters of the vehicle, the method comprising: obtaining a maximum sustained lateral acceleration, a maximum sustained longitudinal deceleration, and an acceleration-speed function of the vehicle provided by a method of determining optimal performance parameters of a vehicle according to any of claims 4 to 10;

splitting the route formed as a series of route-positions into a plurality of sub routes each formed as a sub-series of route-positions;

calculating, for each of the sub-routes, a turn radius depending on the route- positions forming the sub-route;

calculating, for each of the sub-routes, an initial speed and a final speed of a current sub-route, the initial speed of the current sub-route being equal to the final speed of a previous sub-route, and the final speed of the current sub-route being calculated depending on the initial speed and the turn radius of the current sub-route and the maximum sustained lateral acceleration and the acceleration-speed function of the vehicle; adjusting, for each of the sub-routes, the initial speed of the current sub-route depending on the final speed and the turn radius of the current sub-route and the maximum sustained longitudinal deceleration of the vehicle, and assuming said adjusted initial speed as the initial speed of the current sub-route;

calculating, for each of the sub-routes, an optimal time of completing the current sub-route by the vehicle depending on a length, the final speed and the initial speed of the current sub-route;

obtaining the optimal time of completing the route by the vehicle based on summing all the sub-route optimal times.

15. A method according to claim 14, wherein the method of determining optimal performance parameters of the vehicle is repeated according to predefined repetition criterion or criteria; and wherein

the maximum sustained lateral acceleration, the maximum sustained longitudinal deceleration, and the acceleration-speed function of the vehicle are obtained from last repetition.

16. A method according to claim 14 or 15, wherein calculating the final speed of the current sub-route comprises:

calculating a maximum final speed based on the initial speed of the current sub route and a maximum longitudinal acceleration outputted by the acceleration-speed function of the vehicle depending on the initial speed of the current sub-route;

calculating a required lateral acceleration of the current sub-route depending on the turn radius and the maximum final speed of the current sub-route;

verifying whether the required lateral acceleration of the current sub-route is not above the maximum sustained lateral acceleration of the vehicle, in which case the final speed of the current sub-route is determined equal to the maximum final speed of the current sub-route.

17. A method according to claim 16, further comprising:

verifying whether the required lateral acceleration of the current sub-route is above the maximum sustained lateral acceleration of the vehicle, in which case the final speed of the current sub-route is determined depending on the turn radius of the current sub route and the maximum sustained lateral acceleration of the vehicle.

18. A method according to any of claims 14 to 17, wherein adjusting the initial speed of the current sub-route comprises: calculating an estimated lateral acceleration of the current sub-route depending on the turn radius and the final speed of the current sub-route;

calculating a longitudinal deceleration of the current sub-route based on the estimated lateral acceleration of the current sub-route and on a maximum combined acceleration depending on the maximum sustained lateral acceleration and the maximum sustained longitudinal deceleration of the vehicle;

calculating an alternative initial speed depending on the final speed and on the longitudinal deceleration of the current sub-route;

verifying whether the initial speed of the current sub-route is above the alternative initial speed, in which case the initial speed of the current sub-route is updated with the alternative initial speed.

19. A method according to claim 18, further comprising:

verifying whether the initial speed of the current sub-route is not above the alternative initial speed, in which case the initial speed of the current sub-route remains unaltered.

20. A method according to any of claims 18 or 19, wherein the maximum combined acceleration is determined based on following formula:

max _combined_accel = g max _lat_accel² + max Jong _decel² wherein max _combined_accel is the maximum combined acceleration, max Jat_accel is the maximum sustained lateral acceleration of the vehicle, and max Jong_decel is the maximum sustained longitudinal deceleration of the vehicle.

21. A method of determining performance of a driver driving a vehicle to complete a route, the method comprising:

measuring a duration of completing the route by the driver driving the vehicle; obtaining an optimal time provided by a method according to any of claims 14 to 20 for determining the optimal time of completing the route by the vehicle taking into account optimal performance parameters of the vehicle; and

determining the performance of the driver by comparing the measured duration of completing the route and the determined optimal time of completing the route.

22. A system for determining one or more optimal performance parameters of a vehicle equipped with a GNSS device travelling or having travelled a route, the system comprising: a GNSS module configured to obtain a sequence of GNSS-records produced by the GNSS device during the travelled route, each of the GNSS-records including a position detected by the GNSS device and a time of detection of said position;

a splitting module configured to split the sequence of GNSS-records into a plurality of sub-sequences of GNSS-records;

an acceleration module configured to calculate, for each of the sub-sequences, an acceleration depending on the positions and times of the GNSS-records in the sub sequence;

a max-sustained-acceleration module configured to determine a maximum sustained acceleration by calculating an average value of a selection of higher calculated accelerations.

23. A computing system for determining one or more optimal performance parameters of a vehicle equipped with a GNSS device travelling or having travelled a route, said computing system comprising a memory and a processor, embodying instructions stored in the memory and executable by the processor, and the instructions comprising functionality or functionalities to execute a method according to any of claims 1 to 21.

24. A computer program comprising program instructions for causing a computing system to perform a method according to any of claims 1 to 21.

25. A computer program according to claim 24 embodied on a storage medium and/or carried on a carrier signal.

26. A vehicle including a system according to claim 22 for determining one or more optimal performance parameters of the vehicle.