METHOD FOR OPERATING A VEHICLE

Abstract

The present disclosure generally relates to a computer implemented method for operating a vehicle (104, 106, 108), specifically in relation to efficient transporting of a predefined cargo. The present disclosure also relates to a corresponding arrangement and computer program product.

Claims

1. A computer implemented method for operating a vehicle, comprising: receiving an indication of a route for transporting a predefined cargo, the indication of the route comprising a start and a destination, the route comprising a plurality of segments, receiving an indication of a required time of arrival at the destination, determining a speed profile for travelling the route, determining a relaxation coefficient for the speed profile, and controlling the vehicle based on a combination of the speed profile and the relaxation coefficient, wherein: the relaxation coefficient is dependent on a combination of a delay risk for the route and a penalty for arrival at the destination after the required time of arrival, the speed profile is adapted for the plurality of segments of the route to be travelled, the relaxation coefficient is applied for controlling how strict the speed profile is to be followed, and the delay risk for the route is determined by applying a machine learning scheme to simulated or historical data for the route.

2. The method of claim 1, wherein the required time of arrival at the destination includes a time of arrival range.

3. The method of claim 1, wherein the route comprises a predetermined time range for arriving at the start of the route.

4-6. (canceled)

7. The method of claim 1, wherein the speed profile is determined based on at least one of a desired maximum speed for the vehicle or a maximum legal speed for the vehicle at a section of the route.

8. The method of claim 1, wherein the method is performed by an electronic control unit on-board the vehicle.

9. The method of claim 1, wherein the method is performed by a cloud server, the cloud server being network connected to an electronic control unit of the vehicle.

10. The method of claim 1, wherein the speed profile is least partly optimized for reduced transport energy consumption.

11. A system for controlling a vehicle, the system comprising computing circuitry, wherein the computing circuitry is configured to: receive an indication of a route for transporting a predefined cargo, the indication of the route comprising a start and a destination, the route comprising a plurality of segments, receive an indication of a required time of arrival at the destination, determine a speed profile for travelling the route, determine a relaxation coefficient for the speed profile, and control the vehicle based on a combination of the speed profile and the relaxation coefficient, wherein: the relaxation coefficient is dependent on a combination of a delay risk for the route and a penalty for arrival at the destination after the required time of arrival, the speed profile is adapted for the plurality of segments of the route to be travelled, the relaxation coefficient is applied for controlling how strict the speed profile is to be followed, and the delay risk for the route is determined by applying a machine learning scheme to simulated or historical data for the route.

12. The system of claim 11, wherein the computing circuitry is implemented as an electronic control unit of the vehicle.

13. The system of claim 11, wherein the computing circuitry is implemented using a combination of an electronic control unit of the vehicle and a server arranged remotely from the vehicle.

14. The system of claim 11, wherein the required time of arrival at the destination includes a time of arrival range.

15-17. (canceled)

18. The system of claim 11, wherein the route comprises a predetermined time range for arriving at the start of the route.

19. The system of claim 11, wherein the speed profile is determined based on at least one of a desired maximum speed for the vehicle or a maximum legal speed for the vehicle at a section of the route.

20-21. (canceled)

22. A computer program product comprising a non-transitory computer readable medium having stored thereon computer program means for operating a vehicle, wherein the computer program product is configured to: receive an indication of a route for transporting a predefined cargo, the indication of the route comprising a start and a destination, the route comprising a plurality of segments, receive an indication of a required time of arrival at the destination, determine a speed profile for travelling the route, determine a relaxation coefficient for the speed profile, and control the vehicle based on a combination of the speed profile and the relaxation coefficient, wherein: the relaxation coefficient is dependent on a combination of a delay risk for the route and a penalty for arrival at the destination after the required time of arrival, the speed profile is adapted for the plurality of segments of the route to be travelled, the relaxation coefficient is applied for controlling how strict the speed profile is to be followed, and the delay risk for the route is determined by applying a machine learning scheme to simulated or historical data for the route.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0021] With reference to the appended drawings, below follows a more detailed description of embodiments of the present disclosure cited as examples.

[0022] In the drawings:

[0023] FIG. 1 conceptually illustrates a fleet management system, where the concept according to the present disclosure may be applied;

[0024] FIG. 2 provides an illustrative example of an arrangement for controlling a vehicle in line with the present disclosure;

[0025] FIGS. 3A and 3B presents illustrative diagrams of the speed profile and related energy consumption following the concept according to the present disclosure, and

[0026] FIG. 4 shows a flow chart of a method according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0027] The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which currently preferred embodiments of the present disclosure are shown. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided for thoroughness and completeness, and fully convey the scope of the disclosure to the skilled addressee. Like reference characters refer to like elements throughout.

[0028] Referring now to the drawings and to FIG. 1, there is conceptually depicted a fleet management system 100, where the concept according to the present disclosure may be applied as an arrangement/component. The fleet management system 100 comprises a centrally arranged server 102 adapted to control the operation of a plurality of vehicles 104, 106, 108. The illustration as shown in FIG. 1 presents only three vehicles, however fewer or a greater number of vehicles are possible and within the scope of the present disclosure. FIG. 1 further illustrates trucks; however it could be possible to also include other forms of vehicles, such as smaller cargo vehicles, cars, etc.

[0029] The centrally arranged server 102 may in some embodiment be seen as a central “control hub”, providing an entity such as a transport provider with means for communicating instructions to the vehicles 104, 106, 108 for transporting a predefined cargo from a pic-up location (start) to a destination, possibly travelling along a route that has been determined by the server 102 to a drop-off location (end). The server 102 may for example be provided with a network connection, such as the Internet, for facilitating the communication with the vehicles 104, 106, 108.

[0030] The server 102 may also be arranged in communication with one or a plurality of transport buyers 110, 112. FIG. 1 shows only two transport buyers 110, 112, however fewer or a greater number of transport buyers are possible and within the scope of the present disclosure. Also the of transport buyers 110, 112 may be connected to the server 102 using e.g. the Internet.

[0031] During general operation of the fleet management system 100, the transport buyer 110, 112 presents a request to the server 102 in regards to a transport assignment, where the request may for example comprise an indication that a predefined cargo is to be transported from a pick-up location (defining a start of a route) to a delivery location (similarly defining an end of the route), where the goods is to be delivered at a specific delivery time (or no later than a predefined time). The request may also comprise a desire to receive an estimated overall cost (e.g. monetary, environmental, etc.) for transporting the goods.

[0032] Generally, the server 102 may determine if it is possible for one of the vehicles 104, 106, 108 to perform the transport assignment. If at least one of the vehicles 104, 106, 108 indicates that this is possible, then the server 102 may perform a calculation for determining the estimated cost for the transport assignment and provide such feedback to the transport buyer 110, 112. The estimated cost for the transport assignment may for example be determined by taking into account the distance to be travelled (from start to end of the route), the fuel and operational cost for the distance to be travelled, personnel cost (driver, loading/unloading personnel, etc.), the predefined cargo (such as e.g. weight, type of cargo, etc.), the time of the day to perform the transportation assignment, the urgency to perform the transportation assignment, etc.

[0033] The server 102 may in line with the present disclosure also determine a speed profile for travelling the route, where the speed profile is formulated to ensure that operation of the vehicle 104, 106, 108 fulfills current regulations (e.g. maximum speed for different road segments) as well as is to arrive at the specific delivery time. The speed profile is typically adapted for different segments of the route to be travelled, i.e. where the speed is set slower (such as when traveling within a city environment, etc.) for some segments as compared to other (such as when traveling at a highway, etc.).

[0034] In line with the present disclosure, it may be possible to allow the planning of the transportation assignment, and as a consequence the determination of the estimated cost, to be dependent on the above mentioned relaxation coefficient. Specifically, the server 102 may request the transport buyer 110, 112 to additionally present an indication of a penalty for arrival at the destination after the required time (and/or time period) of arrival (including not arriving before a specified time).

[0035] The indication of the penalty for arrival at the destination after the required time of arrival may then be correlated with an e.g. predetermined delay risk for the route. That is, the server 102 may, based on a determined route for transporting the predefined cargo from the start to the end, determine how much time delay that could be expected for performing the transport assignment to ensure that the cargo arrives as requested by the transport buyer 110, 112.

[0036] In accordance to the present disclosure it may for example be possible to base the determination of the delay risk (for example defined as a span of minutes or hours for the route) on previously collected data for the specific route to be travelled for performing the transport assignment. The delay risk may also or alternatively be based on combining previously collected data for a plurality of separate or overlapping segments relating to the route. That is, delay risks for different segments of the road may be combined for determining the “overall” delay risk for the road.

[0037] It may also be possible to include a machine learning process for enhancing the determination of the delay risk. For example, the historically collected data relating to the route (or segments of the route), e.g. stored in database 114, may be processed by the server 102, using the machine learning process, to estimate the delay risk. Feedback from a performed transport mission may then be used for adapting the machine learning process such that future delay risk determinations are becoming more correct as compared to the actual case when performing the transport assignment.

[0038] Preferably, the overall cost determination that is provided to the transport buyer 110, 112 may be performed by the server 102 using a machine learning process. Specifically, the database 114 may be arranged to store the actual costs of performed transport assignments (as well as what type of cargo that was transported, the route, the actual delay, etc.), where this information then may be used by the machine learning process for determining future cost estimations.

[0039] FIG. 2A provides an example of a speed profile 202 that may be determined by the server 102. The speed profile 202 may for example be formulated for ensuring that the vehicle 104, 106, 108 is arriving at the destination at the specific delivery time, in some embodiments optimized for minimizing the wear of the vehicle 104, 106, 108, for minimizing the fuel consumption, or for minimizing the environmental impact of performing the transport mission.

[0040] FIG. 2A further shows a maximum speed limit 204 for the route, form the start to the end of the route. Additionally, in FIG. 2A the route is illustrated to comprise three separate segments 206, 208, 210. The route may of course comprise more than or fewer than three segments.

[0041] Still further, FIG. 2A illustrates the concept with the relaxation coefficient. That is, the determined relaxation coefficient, shown with line 212, exemplifies how is may be possible to implement a form of flexibility in regards to the transport assignment. As shown, the relaxation coefficient defines how strict the vehicle 104, 106, 108 must follow the determined speed profile 202. If for example it is determined that the risk of delay is high, then the speed profile 202 must be followed in a stricter manner as compared to a situation where the delay risk is lower. Additionally, if the transport buyer 110, 112 as indicated that there is an in comparison large penalty for arrival at the destination after the required time of arrival it is desirable to follow the speed profile 202 in a stricter manner as compared to the situation where the penalty for arrival at the destination after the required time of arrival is in comparison low. As is seen in FIG. 2A, the relaxation coefficient must not necessary be linear for the complete route, meaning that the relaxation coefficient may be “stricter” for some segments of the route as compared to other.

[0042] In the illustration shown in FIG. 2A the relaxation coefficient is exemplified as a value between 0-100, where a larger relaxation coefficient is an indication that the speed profile 202 may be followed in a less strict manner as compared to a situation where the relaxation coefficient is small. In some embodiments it may be a possibility to allow the relaxation coefficient to be larger at the start of the transport assignment as compared to at the end of the transport assignment.

[0043] A result of that the relaxation coefficient is low (e.g. below 50 as exemplified in FIG. 2A) is that the driver of the vehicle 104, 106, 108 may be allowed to operate his vehicle in a manner that e.g. reduces the fuel consumption. As an example, in case the relaxation coefficient is low the driver may for example be allowed to actively reduce speed when driving uphill, as compared to a case where the speed profile 202 is to be strictly followed. A further example in this regard is presented in FIG. 2B. Specifically, FIG. 2B illustrates an exemplary correlation between a delay of arriving at the end of the route and the energy consumption for operating the vehicle from the start to the end of the route. As is seen, a large delay may in some situations allow for a lower energy consumption as compared to the case where vehicle 104, 106, 108 is arriving just in time (or just after=small delay) at the end of the route.

[0044] In FIG. 2B the line 214 is representative of the arrival time requested by the transport buyer 110, 112. Correspondingly, the lines 216, 218 represents the energy consumption in relation to different strategies for when to arrive at the end of the route, where line 216 represents an in comparison short delay (in time) and the line 218 represents an in comparison larger delay. As is apparent, less energy has been consumed in the case where the route has taken in comparison a longer time to travel.

[0045] The description above has been presented in relation to a client-server environment, where the server 102 has performed the determination of the speed profile 202 as well as the relaxation coefficient 214. With further reference to FIG. 3, it should be understood that the concept according to the present disclosure could be implemented “on-board” the vehicle 104, 106, 108.

[0046] Specifically, the exemplified vehicle 104 may comprise an electronic control unit (ECU) 302 being a component in an arrangement adapted for performing the method according to the present disclosure. The ECU 302 may for example be manifested as a general-purpose processor, an application specific processor, a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, a field programmable gate array (FPGA), etc.

[0047] The processor may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory. The memory may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description. The memory may include volatile memory or non-volatile memory. The memory may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description. According to an exemplary embodiment the memory is communicably connected to the processor (e.g., via a circuit or any other wired, wireless, or network connection) and includes computer code for executing one or more processes described herein.

[0048] The processor may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory. The memory may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description. The memory may include volatile memory or non-volatile memory. The memory may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description. According to an exemplary embodiment the memory is communicably connected to the processor (e.g., via a circuit or any other wired, wireless, or network connection) and includes computer code for executing one or more processes described herein.

[0049] It should be understood that the ECU 302 may be included with a new vehicle, or retrofitted at a later stage.

[0050] In summary, with further reference to FIG. 4, the present disclosure relates to a computer implemented method for operating a vehicle 104, 106, 108, comprising the steps of receiving, S1, an indication of a route for transporting a predefined cargo, the indication of the route comprising a start and a destination, receiving, S2, an indication of a required time of arrival at the destination, determining, S3, a speed profile 202 for travelling the route, determining, S4, a relaxation coefficient 212 for the speed profile 202, wherein the relaxation coefficient 212 is dependent on a combination of a delay risk for the route and a penalty for arrival at the destination after the required time of arrival, and controlling, S5, the vehicle 104, 106, 108 based on a combination of the speed profile 202 and the relaxation coefficient 212.

[0051] Advantages following the present disclosure for example include the possibility to allow different missions for transporting cargo to have relaxation coefficients and thus to control the vehicles performing the transporting missions differently. This will accordingly result in different “costs” for the different transport missions. Generally, the stricter the speed profile must be followed the higher the cost for operating the vehicle.

[0052] The present disclosure contemplates methods, devices and program products on any machine-readable media for accomplishing various operations. The embodiments of the present disclosure may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwired system. Embodiments within the scope of the present disclosure include program products comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor.

[0053] By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0054] Although the figures may show a specific order of method steps, the order of the steps may differ from what is depicted. In addition, two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps. Additionally, even though the disclosure has been described with reference to specific exemplifying embodiments thereof, many different alterations, modifications and the like will become apparent for those skilled in the art.

[0055] Variations to the disclosed embodiments can be understood and effected by the skilled addressee in practicing the claimed disclosure, from a study of the drawings, the disclosure, and the appended claims. Furthermore, in the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality.