SE541328C2

SE541328C2 - Method and control arrangement for planning and adapting a vehicle transportation route

Info

Publication number: SE541328C2
Application number: SE1751460A
Authority: SE
Inventors: Anders Appelsved; Mathias Zetterfeldt; Neil Coghlan
Original assignee: Scania Cv Ab
Priority date: 2017-11-28
Filing date: 2017-11-28
Publication date: 2019-07-09
Also published as: DE102018008718A1; SE1751460A1

Abstract

Method (400) and control arrangement (130), for planning and adapting transportation route (110) of a vehicle (100), driving to a destination (220). The method (400) comprises: obtaining (401) information related to vehicle starting point (210) and destination (220); obtaining (403) a target arrival time; determining (404) candidate routes (110, 111); collecting (407) information affecting vehicle transportation time, comprising: historical traffic data, from a first data source (140a); and live traffic data, from a second data source (140b), given a respective weight on different road sections (110a, 110b, 110c. 110d, 110e) of the route (110, 111), depending on a distance to the vehicle (100); calculating (408) required transportation time of the vehicle (100); selecting (409) transportation route (110), based on the made calculation (408); determining (410) a starting time for the vehicle (100); and outputting (412) information to vehicle driver concerning the transportation route (110) and the starting time.

Description

METHOD AND CONTROL ARRANGEMENT FOR PLANNING AND ADAPTING A VEHI-CLE TRANSPORTATION ROUTE TECHNICAL FIELD This document discloses a control arrangement and a method in a control arrangement. More particularly, a method and a control arrangement is disclosed, for planning and adapting transportation route of a vehicle driving from a starting point to a destination.

BACKGROUND Connected services has become a strong growth area, whereby intelligent solutions that take advantage of the decreasing cost in computational resources can provide vehicle owners with significant benefits. Existing navigation and routing solutions do not take advantage of the possibilities to optimise routes to achieve lower operational costs for logistics services.

A driver typically does not become aware of a traffic disturbance such as e.g. a traffic jam before he/ she find himself/ herself queuing in the traffic jam; or at a moment when it is too late to select another route, or select another time of the day to make the journey.

For avoiding such a situation, the driver may try to get information in advance, e.g. by obtaining local news, weather forecast information, traffic information on the internet, etc. However, data on e.g. weather, traffic, local news, road data or location of the closest service station can be retrieved from a number of different sources for every local area. This fact makes it difficult to decide upon which data source is most reliable at a certain time, especially for vehicles driving long distances, across country borders and over several days’ time.

Further, perhaps more importantly, the driver is not necessarily helped by the obtained information. He/ she may be informed about an upcoming traffic jam in advance; however, without being offered any alternative route planning option.

Another problem for the driver is that he/ she often has a time schedule to keep during a journey, in order to arrive at certain waypoints at a target time, e.g. ferry departure times, bridge openings, etc. The consequence of arriving too late at the waypoint may then be that the final delivery becomes very delayed. For this reason, the driver often drives at maximum speed in order to have a safety margin for arriving in time to the ferry (or corresponding waypoint). This means that the driver, in case the traffic flow is undisturbed, he/ she has to sit and wait when arriving at the waypoint.

Certain goods such as food, live animals, etc., are very sensitive for delayed transportation and food may even be come destroyed if delivered too late.

Also, when delivering goods such as e.g. industry products at a destination quay, the vehicle is often scheduled a relatively narrow time slot for delivering the goods, e.g. 15 minutes. In case the vehicle is delayed, the driver has to wait for a free time slot in-between other deliveries, which may take considerable time, thereby causing additional delivery delay. Other goods delivery, subject to “just in time” logistics, are associated with hefty penalties if delivered too late. For avoiding these scenarios, the driver normally prefers having a safety margin for avoiding arriving too late at the destination. Thereby, a driver behaviour of driving at maximum speed for arriving as early as possible to the destination is encouraged, leading to unnecessary high fuel consumption.

It would be desired to provide a route planning tool for the driver, assisting him/ her in planning and routing the route to the destination.

There are several distinct problems that may cause problems in the traffic, which are related to usage of obsolete or incorrect data, or erroneous assumptions. It appears that further development is required for reducing problems associated with route planning of a vehicle.

SUMMARY It is therefore an object of this invention to solve at least some of the above problems and achieve a proactive routing.

According to a first aspect of the invention, this objective is achieved by a method for planning and adapting transportation route of a vehicle, driving from a starting point to a destination. The method comprises obtaining information related to starting point, and destination of the vehicle. Further, the method also comprises obtaining a target arrival time of the vehicle, for arriving to the destination. The method also comprises determining a number of candidate routes from the starting point to the destination. In addition, the method furthermore comprises collecting information affecting transportation time of the vehicle while driving along the candidate routes, comprising historical traffic data, from a first data source; and live traffic data, from a second data source. The historical traffic data and the live traffic data are given a respective weight on different road sections of the route, depending on a distance between the vehicle and the road section. Furthermore, the method also comprises calculating required transportation time of the vehicle, while driving along the respective candidate routes, based on the obtained information and the collected information. The method also comprises selecting transportation route out of the determined candidate routes, based on the made calculation. In addition, the method comprises determining a starting time for the vehicle, for departing from the starting point towards the destination along the selected transportation route. Additionally, the method also comprises outputting information to a driver of the vehicle concerning the selected transportation route and the determined starting time.

According to a second aspect of the invention, this objective is achieved by a control arrangement for planning and adapting transportation route of a vehicle, driving from a starting point to a destination. The control arrangement is configured to obtain information related to starting point, and destination of the vehicle, via a communication device. Further, the control arrangement is configured to obtain a target arrival time of the vehicle, for arriving to the destination, via a communication device. In addition, the control arrangement is furthermore configured to determine a number of candidate routes from the starting point to the destination. Also, the control arrangement is configured to collect information affecting transportation time of the vehicle while driving along the candidate routes. The information comprises historical traffic data, from a first data source, and live traffic data, from a second data source. The historical traffic data and the live traffic data are given a respective weight on different road sections of the route, depending on a distance between the vehicle and the road section. In further addition, the control arrangement is configured to calculate required transportation time of the vehicle, while driving along the respective candidate routes, based on the obtained information and the collected information. The control arrangement is furthermore configured to select transportation route out of the determined candidate routes based on the made calculation. In addition, the control arrangement is configured to determine a starting time for the vehicle, for departing from the starting point towards the destination along the selected transportation route. Also, the control arrangement is further configured to output information to a driver of the vehicle concerning the selected transportation route and the determined starting time via an output device.

Thanks to the described aspects, by collecting historical traffic data, and live traffic data of the traffic flow situation at different road sections, and calculate required transportation time and selecting transportation route based on a weighted algorithm, wherein different weights are given to the historical traffic data, and the live traffic data based on the distance between the vehicle and the road section, an improved prediction of the traffic situation along the route of the vehicle, at different route sections at different moments in time could be made. Thereby a better planning may be made of the transportation of the vehicle. Densely trafficked or temporarily blocked route segments may be avoided. Also, route segments suffering from rush hour traffic during certain time periods may be avoided during these periods. It thereby becomes possible to improve a time schedule to keep during transportation, with diminished safety margin, while assuring that the vehicle arrives to the destination in time. Hereby, fuel is saved as the vehicle may drive at reduced speed, the stress of the driver is reduced as he/ she could rely on the transportation time prediction to a higher degree, and the risk of late arrival to the destination is reduced due to the improved prediction of the transportation time.

Other advantages and additional novel features will become apparent from the subsequent detailed description.

FIGURES Embodiments of the invention will now be described in further detail with reference to the accompanying figures, in which: Figure 1 illustrates a side view of a vehicle according to an embodiment.

Figure 2A illustrates an example of a route from a starting point to a destination.

Figure 2B illustrates an example of a vehicle driving on a route, from a starting point to a destination.

Figure 2C illustrates differences between prior art driving and pro-active driving.

Figure 2D illustrates a vehicle driving on a route, from a starting point to a destination according to an embodiment.

Figure 2E illustrates a scenario where a vehicle driving on a route, from a starting point to a destination is re-routed, according to an embodiment.

Figure 3 schematically illustrates a vehicle interior according to an embodiment.

Figure 4 is a flow chart illustrating an embodiment of a method.

Figure 5 is an illustration depicting a system according to an embodiment.

DETAILED DESCRIPTION Embodiments of the invention described herein are defined as a control arrangement and a method in a control arrangement, which may be put into practice in the embodiments described below. These embodiments may, however, be exemplified and realised in many different forms and are not to be limited to the examples set forth herein; rather, these illustrative examples of embodiments are provided so that this disclosure will be thorough and complete.

Still other objects and features may become apparent from the following detailed description, considered in conjunction with the accompanying drawings. It is to be understood, however, that the drawings are designed solely for purposes of illustration and not as a definition of the limits of the herein disclosed embodiments, for which reference is to be made to the appended claims. Further, the drawings are not necessarily drawn to scale and, unless otherwise indicated, they are merely intended to conceptually illustrate the structures and procedures described herein.

Figure 1 illustrates a vehicle 100, driving in a driving direction 105. The vehicle 100 may drive on a transportation route 110.

The vehicle 100 may comprise e.g. a truck, a car, a motorcycle, a multi-passenger vehicle such as a bus, a coach or any similar vehicle or other means of conveyance. The vehicle 100 may be driver-controlled or autonomously controlled driverless vehicle in different embodiments. However, for enhanced clarity, the vehicle 100 is subsequently described as having a driver.

The vehicle 100 comprises a communication device 120, configured for wireless communication. The communication device 120 may comprise e.g. a wireless communication device; an information/ entertainment device comprised in the vehicle 100, or carried by the vehicle driver or a passenger (if any); an output unit configured to output information on a screen, via a loudspeaker, by means of projection, etc. In embodiments wherein the communication device 120 comprises a mobile communication device of the driver, it may be represented by e.g. a smart phone, a computer tablet, a smart watch, a lap top computer, etc.

The communication device 120 may communicate over a cellular network via a base station 135, with a control arrangement 130, having access to one or several data sources 140a, 140b, 140c, 140d of one or several service providers. The data sources 140a, 140b, 140c, 140d may comprise for example databases, or road-side sensors such as cameras, etc.

The control arrangement 130 may be a vehicle external, central unit managing a plurality of vehicles 100, such as e.g. vehicles owned by the same owner, vehicles of the same brand, vehicles of the same type, vehicles enumerating on services provided by the control arrangement 130, etc. The solution can be either server-based, or cloud-based solution in different embodiments. However, in some alternative embodiments, the control arrangement 130 may be comprised in the vehicle 100.

The data sources 140a, 140b, 140c, 140d may be for example different data bases and / or different service providers, such as e.g. live traffic data, metrological data, historical traffic data, seasonal influences on traffic patterns, different weather forecast services, road map services, traffic congestion surveillance services, road work information services, information concerning certain scheduled traffic influencing events such as ferry departures, bridge openings, etc., which may have different levels at different geographical areas and / or at different times of the day / times of the year. Other data sources 140a, 140b, 140c, 140d may comprise road data (maps and geographical coordinates), and various restrictions of the road such as crossing bridges limiting the maximum height of the vehicle 100 on that road, maximum allowed axle weight, vehicle weight, road segments crossing sensitive areas where vehicles delivering certain dangerous goods is prohibited. Road data may be comprised in a data sources 140a, 140b, 140c, 140d, comprising map data with road information such as road routes, road inclinations, curvature, information concerning number of files of different road sections, quality of different road segments, etc. The illustrated example of four data sources 140a, 140b, 140c, 140d is merely an example. In other embodiments, considerably more data sources 140a, 140b, 140c, 140d may be utilised.

The wireless communication interface may comprise or at least being inspired by radio access technologies such as e.g. 5G (5th generation mobile networks), 4G (4th generation mobile networks), 3GPP LTE (3rd Generation Partnership Project Long Term Evolution), LTE-Advanced, UMTS (Universal Mobile Telecommunications System), GSM (Groupe Spécial Mobile), GSM/ EDGE, WCDMA, Time Division Multiple Access (TDMA) networks, Frequency Division Multiple Access (FDMA) networks, Orthogonal FDMA (OFDMA) networks, Single-Carrier FDMA (SC-FDMA) networks, Worldwide Interoperability for Microwave Access (WiMax), or Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA) Evolved Universal Terrestrial Radio Access (E-UTRA), Universal Terrestrial Radio Access (UTRA), GSM EDGE Radio Access Network (GERAN), 3GPP2 CDMA technologies, e.g., CDMA2000 1x RTT and High Rate Packet Data (HRPD), or similar, just to mention some few options, via a wireless communication network.

Many satellite navigation systems have an Application Programming Interface (API) that provides access to historical traffic patterns for most roads. This historical data may be accessible in e.g. 10 minute segments for each day of the week. Existing routing solutions typically use this historical traffic data to provide users with approximate journey times, which may be accessed via the data source 140a, 140b, 140c, 140d.

The provided solution aims at minimising driver hours and fuel consumption of the vehicle 100, while assuring that the vehicle 100 reaches the destination 220 in time, and also reducing driver stress of not arriving in time for enhancing the ergonomic situation of the driver.

Various input values concerning the vehicle 100 are collected, such as e.g. start location, waypoints, final destination, desired arrival time, and any financial penalties for late arrival, various truck attributes such as vehicle geometry, load weight, weight per axle, vehicle weight, additional information concerning payload such as hazardous materials, spoilable goods - i.e. maximum allowed journey time. In some embodiments, this data may be prestored in a database associated with an identity reference of the vehicle 100, and the driver just has to identify the vehicle 100 with the unique identity reference. Alternatively, the number of the licence plate of the vehicle 100 (which typically is a unique identity reference) may be detected by a road side camera for identifying the vehicle 100 and associate it with the time and geographical position of the road segment of the camera.

The current location of the vehicle 100, or a future location, as well as information concerning destination and / or preferred route 110 may be extracted from a navigator of the vehicle 100, or inputted by the driver. In some embodiments, this information may be repeatedly or continuously determined and provided to the control arrangement 130.

Further, a link, or information concerning vehicle tachograph data such as drive/ rest time may be provided. The control arrangement 130 may thereby become aware of the current driving/ resting/ working time status of the vehicle driver and by knowledge of local driving time regulations, the control arrangement 130 may plan and suggest resting time stops for the driver based on the extracted tachograph data.

In case the control arrangement 130 cannot access the tachograph data, or in case the vehicle 100 does not comprise a tachograph, it may be assumed that the driver is well rested when starting driving from the starting point.

The control arrangement 130 may further collect information affecting transportation time of the vehicle 100 from the data sources 140a, 140b, 140c, 140d; e.g. live traffic data, metrological data, seasonal influences on traffic patterns, etc. The data sources 140a, 140b, 140c, 140d may be monitored continuously during the voyage of the vehicle 100, to acquire reliable data and enable continuous rerouting of the vehicle 100.

Temperature may be an important parameter to obtain for temperatures around/ below zero degrees Celsius, as it influences the driving conditions of the road, i.e. ice formation. However, for temperatures over e.g. three degrees Celsius, the temperature is less critical, although the temperature influence may influence friction between the road and the tyres, the aerodynamic resistance, etc. However, precipitation such as rainfall, snowfall, etc., may always be critical for the vehicle 100 during driving, as it may influence visibility and braking capacity.

The current temperature may be obtained either from road side sensors along the route 110, from temperature sensors in other vehicles situated at the route, or from a metrological service, for example.

Current and / or historical traffic density along the planned route 110 is naturally an important factor, influencing the driving time of the vehicle 100. These factors may be weighted differently for different sectors of the route 110, depending on the distance between the vehicle 100 and the particular sector. Thus, for vehicle sectors far away from the vehicle 100, the historical traffic density may have 100% influence on a computation algorithm for predicting the traffic density when the vehicle 100 arrives to the road sector; while the detected current traffic density may have 100% influence on the computation algorithm at the current location of the vehicle 100, and a gradual scale there between. This is further illustrated in Figure 2B and discussed in the corresponding description text.

The historical traffic density along the planned route 110 may be different for different times of the day, different days of the week, periods of the year, etc. Thus, the current time, day, period, season etc., may be determined and the historical traffic density for a particular route segment at the corresponding time/ day / period, etc., may be determined.

Based on the obtained information related to the transportation route 110 of the vehicle 100 and the collected information affecting transportation time of the vehicle 100 while driving to the destination, the control arrangement 130 may calculate an appropriate route and a required transportation time of the vehicle 100, for driving from the starting point to the destination on the transportation route 110. Further, by obtaining a desired arrival time of the vehicle 100, to the destination from the driver, an appropriate starting time for the vehicle 100 may be determined. Also, an ideal vehicle speed may be calculated, based on road grade and vehicle weight.

The calculation may be based on a Neural Network algorithm and / or a Multi-Objective Genetic Optimisation process in some embodiments.

An artificial neural network is a computing system inspired by biological neural networks that constitute animal/ human brains. Such systems learn (progressively improve performance) to do tasks by considering examples, generally without task-specific programming. For example, in image recognition, the neural network might learn to identify images that contain a vehicle on a road segment by analysing example images that have been manually labelled as "vehicle" or "no vehicle" and using the analytic results to identify vehicles in other images of different road segments. Neural networks may with advantage be applied in applications difficult to express in a traditional computer algorithm using rule-based programming.

The neural network is based on a collection of connected units called artificial neurons (analogous to biological neurons in an animal/ human brain). Each connection (synapse) between neurons can transmit a signal to another neuron. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it. Neurons may have a state, generally represented by real numbers, typically between 0 and 1, or 0-100%. Neurons and synapses of the neural network may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Further, the neural network may have a threshold such that only if the aggregate signal is below (or above) that level is the downstream signal sent.

Multi-objective optimisation (also known as multi-objective programming, vector optimisation, multi-criteria optimisation, multi-attribute optimisation or Pareto optimisation in different contexts) is an area of multiple criteria decision making, that is concerned with mathematical optimisation problems involving more than one objective function to be optimised simultaneously. Multi-objective optimisation may with advantage be applied in applications in complex scenarios where optimal decisions need to be taken in the presence of trade-offs between two, or more, conflicting objectives, such as minimising the fuel consumption of the vehicle 100 while minimising the risk of arriving too late to the destination, and minimising the working time of the driver.

For a nontrivial multi-objective optimisation problem, typically no single solution may exist, that simultaneously optimises each objective. In that case, the objective functions are said to be conflicting, and there may exist a plurality of Pareto optimal solutions.

A solution may be referred to as non-dominated, Pareto optimal, Pareto efficient or noninferior, if none of the objective functions can be improved in value without degrading some of the other objective value. Without additional subjective preference information, all Pareto optimal solutions may be considered equally good. The goal may be to find a representative set of Pareto optimal solutions, and / or quantify the trade-offs in satisfying the different objectives, and / or finding a single solution that satisfies the subjective preferences of a human decision maker designing the computation algorithm.

Having made computations for estimating transportation time of different alternative transportation routes, a selection of the best transportation route for the vehicle 100 may be selected, and an estimation of the transportation time for the vehicle 100, comprising an estimated time schedule for passing certain waypoints along the route may be determined. The calculation may also comprise for example suggested stops for break, based on tachograph data; and / or suggested stops for fuel filling based on obtained data concerning fuel level of the vehicle 100, in some embodiments. Also meal stops, or coffee/ tea breaks may be suggested.

The driver may then be informed concerning the determined transportation route 110, the starting time, appropriate rest stops (location and duration) and / or the ideal vehicle speed. This information may be outputted to the driver via a display and / or a loudspeaker in the vehicle 100, via a mobile cellular telephone of the driver, via a smart watch of the driver, etc.

The information may be outputted e.g. as a predicted time schedule with a predicted target time outputted for different waypoints along the route 110. Recommended stops may be highlighted or otherwise marked. Alternatively, voice messages may be outputted to inform the driver when passing waypoints, how the vehicle is proceeding in comparison with the predicted time schedule, and recommend stops.

The driver/ vehicle 100 may thereby avoid for example roads that are likely to be congested at the time period when the vehicle 100 is predicted to pass, for example. In case a weather forecast predict freezing temperatures, fog or rain along the road 110, the estimated transportation time of the vehicle 100 may have to be extended due to an adjusted/ decreased driving speed (due to the weather conditions), which in turn may lead to that allowed driving hours/ working hours of the driver of the vehicle 100 is exceeded, which may force the driver to stop and take a rest, which again causes an adjustment of the estimated arrival time, etc. Possibly, another route may be selected or recommended for avoiding this scenario and reducing/ eliminating delay. By continuously, or at regular configurable or predicted time intervals obtaining data influencing the traffic flow of the route 110, re-perform the calculations and output this updated information to the driver, the speed of the vehicle 100 may be adjusted in order to keep the time schedule of the vehicle 100.

Thanks to the provided solution, fuel consumption and environmental impact of heavy duty road transportation may be reduced. Further, serviceable lifetime of the vehicle 100 is extended. In addition, work hours and stress levels of the vehicle driver are reduced.

Figure 2A is a map illustrating a starting point 210, a destination 220 and a number of alternative routes 110, 111 for the vehicle 100, from the starting point 210 to the destination 220.

When the vehicle 100 contacts the control arrangement 130 for assistance in planning and adapting transportation route of the vehicle 100, information concerning starting point 210, and destination 220 is provided to the control arrangement 130. Also other information may be provided, such as e.g. preferred route 110, 111, waypoints, etc., or the driver.

In some embodiments, the driver may enter the information concerning starting point 210 and destination 220 e.g. via an app in a communication device 120 of the driver. Alternatively, the starting point 210 may be determined to be the current position of the vehicle 100 while the destination 220 of the vehicle 100 may be extracted from a navigator of the vehicle 100.

Based on the obtained starting point 210 and destination 220 of the vehicle 100, the control arrangement 130 may obtain map data from a data source 140a, 140b, 140c, 140d and based there upon, a number of alternative routes 110, 111 may be determined by the control arrangement 130, leading from the starting point 210 to the destination 220. In some cases, e.g. when driving a very short distance or when there is only one available route 110, 111 between the starting point 210 and the destination 220, the determination of candidate routes may comprise only one single route 110, 111.

The control arrangement 130 may then collect information affecting transportation time of the vehicle 100 while driving along the respective candidate routes 110, 111 to the destination 220, from the data sources 140a, 140b, 140c, 140d.

The data sources 140a, 140b, 140c, 140d may respectively comprise information about current traffic situation, current or future weather conditions, planned road works, appeared road accidents, traffic congestion surveillance services, seasonal influences on traffic patterns, on the respective candidate routes 110, 111. It may then be determined, based thereupon, which of the candidate routes 110, 111 leads to shortest transportation time and / or smallest fuel consumption of the vehicle 100 during the transportation.

Further, desired arrival time of the vehicle 100 may be obtained e.g. from the driver of the vehicle 100, or from the vehicle navigator. It may then be calculated when the driver has to start drive from the starting point 210 towards the destination 220 in order to arrive at the desired arrival time.

In some embodiments, breaks and resting stops may be planned and suggested for the driver, based on obtained tachograph data of the driver and / or meal time periods of the day and knowledge about the driver’s last stop. By knowledge of the geographical position of the vehicle 100, local legislations, or alternatively company policy concerning working time may be followed.

Also, an appropriate driving speed may be calculated for the vehicle 100, in order to keep the time table for arriving at the destination 220 at the desired arrival time. In some embodiments, the geographical position and current speed of the vehicle 100 may be continuously provided to the control arrangement 130. The control arrangement 130 may then compare the obtained information of position and speed with a time table of expected values and recalculate appropriate speed, break lengths, etc., in order to arrive on the desired arrival time.

Figure 2B schematically illustrates the vehicle 100 driving from the starting point 210 towards the destination 220, in the driving direction 105 along the route 110.

Current traffic density data and historical traffic density data along the planned route 110 is determined, obtained from different data sources 140a, 140b. A calculation algorithm may then estimate driving time of the vehicle 100, based on a weighted estimation based on the current traffic data and the historical traffic data.

These factors may be weighted differently for different sectors 110a, 110b, 110c, 110d, 110e of the route 110, depending on the distance between the vehicle 100 and the particular sector 110a, 110b, 110c, 110d, 110e.

For example, the sector 110a, in which the vehicle 100 currently is situated, current traffic data may have a weight of 1 (100% influence) while historical traffic data may have the weight 0 (0% influence). In a more distant route sector 110b, the current traffic data may have a weight of 0.8 (80% influence) while historical traffic data may have the weight 0.2 (20% influence). In a further more vehicle distant route sector 100c, the current traffic data may have a weight of 0.6 (60% influence) while historical traffic data may have the weight 0.4 (40% influence). In yet a further distant route sector 10Od, the current traffic data may have a weight of 0.3 (30% influence) while historical traffic data may have the weight 0.7 (70% influence). In more distant route sectors 100e, the current traffic data may have a weight of 0 (0% influence) while historical traffic data may have the weight 1 (100% influence). The described weights are only an arbitrary example to illustrate the principle; the route 110 may be divided into more, or less route sectors 110a, 110b, 110c, 110d, 11 Oe, and the weights may be selected differently for the current traffic data/ historical traffic data.

As the influence of current traffic data / historical traffic data on the computation algorithm for estimating the traffic flow of a particular route sector 110a, 110b, 110c, 110d, 110e may shift as the vehicle 100 approaches, the traffic flow estimation may also change, why a continuous recalculation may be made, as the vehicle 100 proceed towards the destination 210, for making an as correct prediction as possible.

Figure 2C illustrates a comparison between conventional, previously known driving 230, and the provided pro-active driving 240, in a scenario where the vehicle 100 drives from the starting point 210 to the destination 220 along the route 110.

The previously known driving 230, typically starts early with a generous time schedule in order to make the delivery in time. Once started, the driver typically set the cruise control to maximum allowed speed of the vehicle 100 and the road segment in question, until tachograph limitations force him/ her to take a break. In case the driver/ vehicle 100 subscribes to a traffic congestion surveillance service; or alternatively, simply by listening to local radio providing traffic information, the driver may become aware of an ahead traffic jam.

However, although the driver becomes aware of the mobility limitations further down the route 110, it is not much he/ she can do at this stage. The driver gets stuck in traffic jam for a certain time period, e.g. 20 minutes, involving repeatedly stop-start driving. When the traffic congestion eases, the vehicle 100 may increase speed up to the maximum allowed speed and finally reaches the destination 220. In this example, the driver then has to wait 75 minutes at the destination 220 for the delivery time slot, where after the delivery may be made.

The same journey with the same vehicle 100, but according to the provided pro-active driving 240 will become radically different. Firstly, a route 110 and a departure time are determined according to the above described method. By predicting traffic flow, weather conditions, visibility, etc., of the route 110 at the time when the vehicle 100 passes, the departure time and speed of the vehicle 100 may be adjusted, determined and communicated to the driver. The driver commences driving towards the destination 220 at maximum speed. When the tachograph indicates that the driver has to take a break, the driver stops. However, the control arrangement 130 may at this moment obtain updated information concerning traffic flow restrictions down the route 110 and for this reason recommend the driver to extend the break to 60 minutes. The driver may in some embodiments be notified when it is time to continue the driving. Further, the driver may be recommended to decrease the speed with e.g., 10 km/h, in order to avoid the traffic congestion, save fuel and yet arrive in time. In this example, the driver then has to wait 10 minutes at the destination 220 for the delivery time slot, where after the delivery may be made.

Results of the pro-active driving 240 comprises saved fuel, and less driving time, in addition the method reduces stress of the driver and assist him/ her to plan the route in an optimal or at least improved manner.

Figure 2D illustrates an example of different possible paths 110, 111 for the vehicle 100 to select between when driving from the starting point 210 to the destination 220. It may be noted that the indicated first section distance 250 in the figure may be proportionally much longer than the length in the drawing indicates.

At a first moment, the vehicle is situated close to the starting point 210 and the calculation of the transportation time or the vehicle 100 along the different route alternatives 110, 111, the calculations may be based primarily on historical traffic data, why the shortest route 110 may be selected and recommended for the driver.

Figure 2E illustrates the same scenario as has been depicted in Figure 2D, but at a later moment in time. The vehicle 100 has approached the destination 220 considerably and the computations of the vehicle transportation time becomes gradually more and more influenced by the current traffic situation. In the illustrated arbitrary example, an alien object 260 is blocking the previously selected route 110 in an unpredicted way. When a recalculation is made taking the new weights into regard, the outcome may be to recommend the vehicle 100 to instead take the other route 111 to the destination 210, although longer, in order to keep the target arrival time.

Figure 3 illustrates a scenario wherein the vehicle 100 is driving in the driving direction 105 on the road 110, as it may be perceived by the driver of the vehicle 100.

The vehicle 100 may comprise a tachograph 310. The vehicle carried tachograph 310 may automatically record speed and driving distance of the vehicle 100, together with the driver's activity selected from a choice of modes. The drive mode may be activated automatically when the vehicle 100 is in motion, and typically switch to work mode when the vehicle 100 stops. The driver may also select rest and availability modes manually when the vehicle 100 is stationary. It may be assumed that the driver is less attentive when the tachograph 310 has been set into drive mode for a time period exceeding a predetermined or configurable time limit.

The geographical position of the vehicle 100 may be determined by a positioning device 320, or navigator, in the vehicle 100, which may be based on a satellite navigation system such as the Navigation Signal Timing and Ranging (Navstar) Global Positioning System (GPS), Differential GPS (DGPS), Galileo, GLONASS, or the like.

The geographical position of the positioning device 320, (and thereby also of the vehicle 100) may be made repeatedly, such as e.g. continuously at a certain predetermined or configurable time interval, according to various embodiments. This information may in some embodiments be provided to the control arrangement 130, so that the control arrangement 130 is continuously informed about the current position of the vehicle 100. Thereby, the control arrangement 130 may repeatedly recalculate a time schedule and an optimal speed of the vehicle 100; and / or for example determine that the vehicle 100 is not following the determined route 110, which may trigger output of an alert to the driver.

Positioning by satellite navigation is based on distance measurement using triangulation from a number of satellites 330a, 330b, 330c, 330d. In this example, four satellites 330a, 330b, 330c, 330d are depicted, but this is merely an example. More than four satellites 330a, 330b, 330c, 330d may be used for enhancing the precision, or for creating redundancy. The satellites 330a, 330b, 330c, 330d continuously transmit information about time and date (for example, in coded form), identity (which satellite 330a, 330b, 330c, 330d that broadcasts), status, and where the satellite 330a, 330b, 330c, 330d are situated at any given time. The GPS satellites 330a, 330b, 330c, 330d sends information encoded with different codes, for example, but not necessarily based on Code Division Multiple Access (CDMA). This allows information from an individual satellite 330a, 330b, 330c, 330d distinguished from the others' information, based on a unique code for each respective satellite 330a, 330b, 330c, 330d. This information can then be transmitted to be received by the appropriately adapted positioning device comprised in the vehicle 100.

Distance measurement can according to some embodiments comprise measuring the difference in the time it takes for each respective satellite signal transmitted by the respective satellites 330a, 330b, 330c, 330d to reach the positioning device 320. As the radio signals travel at the speed of light, the distance to the respective satellite 330a, 330b, 330c, 330d may be computed by measuring the signal propagation time.

The positions of the satellites 330a, 330b, 330c, 330d are known, as they continuously are monitored by approximately 15-30 ground stations located mainly along and near the earth's equator. Thereby the geographical position, i.e. latitude and longitude, of the vehicle 100 may be calculated by determining the distance to at least three satellites 330a, 330b, 330c, 330d through triangulation. For determination of altitude, signals from four satellites 330a, 330b, 330c, 330d may be used according to some embodiments.

Having determined the geographical position of the vehicle 100 by the positioning device 320 (or in another way), the geographical position of the vehicle 100 may be provided to the control arrangement 130.

In some embodiments, the current geographical position of the vehicle 100 and the computed predicted path of the vehicle 100 may in some embodiments be displayed on the information outputting device.

In some embodiments, the constraint in geographical position and / or time may be calculated by information extracted from the positioning device 320/ navigator, such as current position, target destination, estimated speed, estimated driving direction.

Furthermore, as in the illustrated embodiment, the vehicle 100 may comprise an output device 340. The output device 340, or presentational unit, may be represented by a display, a loudspeaker, a projector, a head-up display, a display integrated in the windshield of the vehicle 100, a display integrated in the dashboard of the vehicle 100, a tactile device, a portable device (e.g. a cell phone or a smart watch) of the vehicle driver/ owner, a set of close-eyes displays (i.e. intelligent glasses/ optical head-mounted displays in the shape of a pair of eyeglasses) of the vehicle driver/ owner, etc.; or a combination thereof. The output device 340 may also comprise a loud speaker or a tactile device in some embodiments, or a combination thereof.

The communication device 120 and the output device 340 may be collocated, or comprised in the same unit in some embodiments, e.g. in a cell phone or a smart watch of the driver.

Thereby, the current geographical position of the vehicle 100, and possibly also driving direction 105 and speed of the vehicle 100 may be determined and be provided to the control arrangement 130 for route planning purposes.

Further the vehicle 100 may comprise a cruise control 350. The cruise control 350, sometimes known as speed control or autocruise, is a system that automatically controls the speed of the vehicle 100. The cruise control 350 may comprise a servomechanism that takes over the throttle of the vehicle 100 to maintain a steady speed as set by the driver, or, in some embodiments, by the control arrangement 130, according to the calculated optimal speed of the vehicle 100. The cruise control 350 may be adaptive in some embodiments, i.e. the vehicle 100 may comprise a sensor for detecting another vehicle in front, and the set vehicle speed may be reduced or adapted to the speed of the vehicle in front.

Figure 4 illustrates an example of a method 400 according to an embodiment. The flow chart in Figure 4 shows the method 400 in a control arrangement 130. The control arrangement 130 may in some embodiments be comprised in a vehicle external structure. However, alternatively, the control arrangement 130 may be comprised in the vehicle 100.

The method 400 aims at planning and adapting transportation route 110 of the vehicle 100, driving from a starting point 210 to a destination 220.

In order to be able to plan and adapt the vehicle transportation route 110, the method 400 may comprise a number of steps 401-412. However, some of these steps 401-412 may be performed solely in some alternative embodiments, like e.g. steps 402, 405-406, and / or 411 . Further, the described steps 401-412 may be performed in a somewhat different chronological order than the numbering suggests. The method 400 may comprise the subsequent steps: Step 401 comprises obtaining information related to the starting point 210, and the destination 220 of the vehicle 100.

The information may e.g. be entered by the driver; or may be extracted from the navigator 320 in different embodiments.

Step 402, which only may be performed in some particular embodiments, comprises obtaining tachygraph data of the driver, from a tachygraph 310 in the vehicle 100.

In case the tachygraph data is not, or could not, be obtained, it may be assumed that the driver is well rested at the beginning of the journey, and working/ driving times may yet be calculated and resting breaks may be suggested, based upon this assumption, in some embodiments.

Step 403 comprises obtaining a target arrival time of the vehicle 100, for arriving to the destination 220.

The target arrival time may be entered by the driver in some embodiments, or extracted from the navigator 320; alternatively obtained from a database of planned transportations.

The obtained target arrival time of the vehicle 100 may in some embodiments comprise information concerning cost for late arrival. The cost may be a financial cost, i.e. a fine. Alternatively, the cost may be a time period that the driver has to wait until the next available delivery timeslot, for example.

Step 404 comprises determining a number of candidate routes 110, 111 from the starting point 210 to the destination 220.

The candidate routes 110, 111 may be extracted from a map database. In some embodiments, a set of 2-5 alternative routes may be preselected, based on length of the respective routes 110, 111. In some embodiments however, only one candidate route 110, 111 is determined.

Step 405, which only may be performed in some particular embodiments, comprises determining geographical position of the vehicle 100. The geographical position may be determined by the navigator 320 of the vehicle 100; or alternatively be entered by the driver. In yet some embodiments, the vehicle 100 may be identified by a road side camera 140b, which thereby may determine the current location of the vehicle 100.

Step 406, which only may be performed in some particular embodiments, comprises obtaining information related to an attribute of the vehicle 100 comprising any of: geometry of the vehicle 100, weight per axle, weight of the vehicle and the cargo, type of vehicle 100, remaining fuel in the tank of the vehicle 100, hazardous material in the cargo, spoilable cargo goods, fuel level of the vehicle 100, urea level of the vehicle 100, etc.

This obtained information may determine limitations on the selected route 110, 111. For example, height of the vehicle 100 may disqualify a route with a crossing bridge lower than the vehicle 100, for example. Other routes 110, 111 may have limitations concerning axle weight, vehicle weight; or concerning allowable cargo.

Step 407 comprises collecting information affecting transportation time of the vehicle 100 while driving along the candidate routes 110, 111. The collected information may comprise historical traffic data, from the first data source 140a, such as a database and live traffic data, from the second data source 140b, such as a sensor or camera situated at a route section 110a, 110b, 110c. 110d, 110e. The historical traffic data and the live traffic data are given a respective weight on different road sections 110a, 110b, 110c. 110d, 110e of the route 110, 111, depending on a distance between the vehicle 100 and the road section 110a, 110b, 110c. 110d, 110e, where the historical data is increasingly weighted as the distance increases.

The collection of information affecting transportation time of the vehicle 100 may be performed repeatedly in some embodiments, such as continuously or at predetermined or configurable time periods.

The live traffic data may be collected from road side sensors such as e.g. surveillance cameras, lasers, photocells, etc., for detecting traffic intensity at a particular road section 110a, 110b, 110c. 11 Od, 110e. The live traffic data may be obtained from one or several road-side sensors, or from one or several sensors in one or several other vehicles, which are situated within or at the road section 110a, 110b, 110c, 110d, 11 Oe, such as a camera therein or by extracting speed information of the vehicles or objects therein such as mobile telephones. The sensors may be of the same or different types.

The collected information affecting transportation time of the vehicle 100 may in some embodiments further comprise metrological data, obtained from at least a third data source 140c.

The collected information affecting transportation time of the vehicle 100 further may comprise traffic influencing event data, from a fourth data source 140d, in some embodiments. The traffic influencing event data may comprise e.g. knowledge concerning planned bridge openings, ferry departure/ arrival times, traffic congestion surveillance services, road work information services, local news concerning accidents, etc.

Step 408 comprises calculating required transportation time of the vehicle 100, while driving along the respective candidate routes 110, 111, based on the obtained 401 information and the collected 407 information, in addition to knowledge of maximum allowed vehicle speed, and / or maximum allowed road speed of different road sections 110a, 110b, 110c, 110d, 110e.

The calculation of the required transportation time of the vehicle 100, may be performed continuously, based on the collected 407 information, and updated respective weights for the historical traffic data and the live traffic data, in some embodiments.

In some embodiments, wherein step 402 previously has been performed, the calculation may comprise determining an appropriate moment for a break for the driver, based on the obtained 402 tachygraph data.

The calculation of the required transportation time of the vehicle 100 may comprise determining an appropriate speed of the vehicle 100, based on the collected 407 information in some embodiments.

In some embodiments, wherein step 406 has been performed, the calculation of the required transportation time may be based on the obtained 406 information.

The calculation of the required transportation time of the vehicle 100 may in some embodiments be adjusted based on the collected 407 metrological data, when they may be expected to influence possible vehicle speed due to decreased adhesion between tyres and road; or due to decreased visibility.

The calculation of the required transportation time of the vehicle 100, the determination of appropriate speed of the vehicle 100, may be performed or adjusted based on road grade of the route 110 and vehicle weight.

The calculation of the required transportation time of the vehicle 100 may be made based on the obtained 403 cost for late arrival.

The calculation of the required transportation time of the vehicle 100 may be adjusted based on the collected 407 traffic influencing event data.

Step 409 comprises selecting transportation route 110 out of the determined 404 candidate routes 110, 111, based on the made calculation 408.

In some embodiments, wherein step 406 has been performed, the selection 409 of transportation route 110 may be based on the obtained 406 information.

The selection of the transportation route 110 may be made for optimisation with regard to any one of: shortest transportation time, lowest combined fuel and urea consumption, shortest working time of the driver, or lowest risk for an accident.

Step 410 comprises determining a starting time for the vehicle 100, for departing from the starting point 210 towards the destination 220 along the selected 409 transportation route 110.

The determination of the starting time for the vehicle 100, may be performed based on road grade of the route 110 and vehicle weight.

The calculation of the required transportation time of the vehicle 100, and the determination 410 of the starting time for the vehicle 100 may be made based on the obtained 403 cost for late arrival.

Step 411, which only may be performed in some particular embodiments, comprises adjusting speed for a cruise control unit 350 of the vehicle 100 to keep, according to the made calculations 408.

Thereby, the vehicle driver could be further released from worries concerning the arrival time which has ergonomical advantages. Also, the driver may instead focus on the environmental traffic situation, which enhances traffic safety.

Step 412 comprises outputting information to a driver of the vehicle 100 concerning the selected 409 transportation route 110 and the determined 410 starting time, on an output device 340.

Information concerning determined breaks, based on tachygraph information obtained from the tachograph of the vehicle 100, may be output to the driver of the vehicle 100.

Furthermore, the information outputted to the driver of the vehicle 100 may also comprise a determined appropriate speed for the vehicle 100, in order to reach the destination 220 in time, i.e. the obtained 403 target arrival time of the vehicle 100.

Figure 5 presents a system 500 for planning and adapting transportation route 110 of a vehicle 100, driving from a starting point 210 to a destination 220.

The system 500 comprises a control arrangement 130 for performing the method 400 according to any, some or all of the previously described method steps 401-412 as described above and illustrated in Figure 4.

The control arrangement 130 aims at planning and adapting transportation route 110 of the vehicle 100, driving from the starting point 210 to the destination 220. The control arrangement 130 is configured to obtain information related to starting point 210, and destination 220 of the vehicle 100, via a communication device 135. Further, the control arrangement 130 also is configured to obtain a target arrival time of the vehicle 100, for arriving to the destination 220, via a communication device 135. In addition, the control arrangement 130 is further configured to determine a number of candidate routes 110, 111 from the starting point 210 to the destination 220. The control arrangement 130 is also configured to collect information affecting transportation time of the vehicle 100 while driving along the candidate routes 110, 111, comprising: historical traffic data, from a first data source 140a, and live traffic data, from a second data source 140b. The historical traffic data and the live traffic data are given a respective weight on different road sections 110a, 110b, 110c, 110d, 110e of the route 110, 111, depending on a distance between the vehicle 100 and the road section 110a, 110b, 110c, 110d, 11 Oe, such as e.g. approximately proportionally to the distance to the vehicle 100. Furthermore, in addition, the control arrangement 130 is configured to calculate required transportation time of the vehicle 100, while driving along the respective candidate routes 110, 111, based on the obtained information and the collected information. The control arrangement 130 is also configured to select transportation route 110 out of the determined candidate routes 110, 111, based on the made calculation. Further, the control arrangement 130 is configured to determine a starting time for the vehicle 100, for departing from the starting point 210 towards the destination 220 along the selected transportation route 110. Also, the control arrangement 130 is additionally configured to output information to a driver of the vehicle 100 concerning the selected transportation route 110 and the determined starting time via an output device 340.

In some embodiments, the control arrangement 130 may also be configured to determining geographical position of the vehicle 100, e.g. via a navigator 230 of the vehicle 100, repeatedly.

The control arrangement 130 may in some embodiments be configured to repeatedly such as e.g. continuously collecting information affecting transportation time of the vehicle 100, and calculate required transportation time of the vehicle 100, based on the collected information, and updated respective weights for the historical traffic data and the live traffic data.

Furthermore, the control arrangement 130 may be configured to obtaining tachygraph data of the driver, from a tachygraph 310 in the vehicle 100. The control arrangement 130 may also be configured to determining an appropriate moment for a break for the driver, based on the obtained tachygraph data; and output information concerning the determined breaks to the driver of the vehicle 100, via an output device 340 of the vehicle 100, or the vehicle driver.

In addition, the control arrangement 130 may alternatively also be configured to determine an appropriate speed of the vehicle 100, based on the collected information, for calculating the required transportation time of the vehicle 100. Further, the control arrangement 130 may be configured to output information to the driver of the vehicle 100 comprises the determined appropriate speed, via the output device 340.

The control arrangement 130 may further be configured to obtain information related to an attribute of the vehicle 100 comprising any of: geometry of the vehicle 100, weight per axle, weight of the vehicle 100, type of vehicle 100, remaining fuel in the tank of the vehicle 100, hazardous material in the cargo, spoilable cargo goods, fuel level of the vehicle 100, urea level of the vehicle 100. The control arrangement 130 may also be configured to calculate the required transportation time, and select transportation route 110 based on the obtained information.

Furthermore, the control arrangement 130 may be configured to collect information affecting transportation time of the vehicle 100 comprising metrological data, from a third data source 140c and calculate the required transportation time of the vehicle 100 with adjustments based on the collected metrological data.

According to some embodiments, the control arrangement 130 may also be configured to calculate the required transportation time of the vehicle 100, determine appropriate speed of the vehicle 100, and determine the starting time for the vehicle 100, based on road grade of the route 110, and vehicle weight.

In further addition, the control arrangement 130 may also be configured to obtain target arrival time of the vehicle 100, comprising information concerning cost for late arrival. Also, the control arrangement 130 may be configured to calculate the required transportation time of the vehicle 100, and determine the starting time for the vehicle 100, based on the obtained cost for late arrival.

According to some alternative embodiments, the control arrangement 130 may be configured to select the transportation route 110 for optimisation with regard to any one of: shortest transportation time, lowest combined fuel and urea consumption, shortest working time of the driver, or lowest risk for an accident.

The control arrangement 130 may further be configured to collect information affecting transportation time of the vehicle 100 further comprises traffic influencing event data, from a fourth data source 140d. The control arrangement 130 may also be configured to calculate the required transportation time of the vehicle 100, adjusted based on the collected traffic influencing event data.

Also, the control arrangement 130 may be configured to adjust speed for a cruise control unit 350 of the vehicle 100 to keep, according to the made calculations.

The system 500 also comprises a communication device 135, configured to communicate with a communication device 120 of the vehicle 100.

Furthermore, the system 500 also additionally comprises at least one data source 140a, 140b, 140c, 140d, comprising information affecting transportation time of the vehicle 100 while driving along the candidate routes 110, 111 to the destination 220.

The system 500 further comprises an output device 340, configured to output information to the driver of the vehicle 100.

The system 500 may also comprise the communication device 120, configured to request and receive information.

In some embodiments, the system 500 also may comprise a tachograph 310; a positioning device 320, such as a navigator; a clock; a memory storage device, a cruise control device 350, etc.

The control arrangement 130 may comprise a receiving unit 510 configured for receiving information via a wired or wireless communication, from the communication device 120.

The control arrangement 130 further may comprise a processing circuit 520 configured for performing various calculations for conducting the method 400 according to at least some of the previously described method steps 401-412.

Such processing circuit 520 may comprise one or more instances of a processing circuit, i.e. a Central Processing Unit (CPU), a processing unit, a processor, an Application Specific Integrated Circuit (ASIC), a microprocessor, or other processing logic that may interpret and execute instructions. The herein utilised expression “processing circuit” may thus represent a processing circuitry comprising a plurality of processing circuits, such as, e.g., any, some or all of the ones enumerated above.

Furthermore, the control arrangement 130 may comprise a memory 525 in some embodiments. The optional memory 525 may comprise a physical device utilised to store data or programs, i.e., sequences of instructions, on a temporary or permanent basis. According to some embodiments, the memory 525 may comprise integrated circuits comprising siliconbased transistors. The memory 525 may comprise e.g. a memory card, a flash memory, a USB memory, a hard disc, or another similar volatile or non-volatile storage unit for storing data such as e.g. ROM (Read-Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM), etc. in different embodiments.

Further, the control arrangement 130 may comprise a signal transmitting unit 530. The signal transmitting unit 530 may be configured for transmitting a command signal to be received by the communication device 120 and possibly also the output device 340.

The previously described steps 401-412 to be performed in the control arrangement 130 may be implemented through the one or more processing circuits 520 within the control arrangement 130, together with computer program product for performing at least some of the functions of the steps 401-412. Thus, a computer program product, comprising instructions for performing the steps 401-412 in the control arrangement 130 may perform the method 400 comprising at least some of the steps 401-412 for planning and adapting transportation route 110 of the vehicle 100, driving from the starting point 210 to the destination 220, when the computer program is loaded into the one or more processing circuits 520 of the control arrangement 130.

The described steps 401-412 thus may be performed by a computer algorithm, a machine executable code, a non-transitory computer-readable medium, or a software instructions programmed into a suitable programmable logic such as the processing circuits 520 in the control arrangement 130 in various embodiments.

The computer program product mentioned above may be provided for instance in the form of a data carrier carrying computer program code for performing at least some of the step 401-412 according to some embodiments when being loaded into the one or more processing circuits 520 of the control arrangement 130. The data carrier may be, e.g., a hard disk, a CD ROM disc, a memory stick, an optical storage device, a magnetic storage device or any other appropriate medium such as a disk or tape that may hold machine readable data in a non-transitory manner. The computer program product may furthermore be provided as computer program code on a server and downloaded to the control arrangement 130 remotely, e.g., over an Internet or an intranet connection.

Further, some embodiments may comprise a vehicle 100, or a vehicle external structure, comprising the control arrangement 130, as described above, for performing the method according to at least some of the described method steps 401 -412.

The terminology used in the description of the embodiments as illustrated in the accompanying drawings is not intended to be limiting of the described method 400, control arrangement 130; computer program, and / or system 500. Various changes, substitutions and / or alterations may be made, without departing from invention embodiments as defined by the appended claims.

As used herein, the term "and/ or" comprises any and all combinations of one or more of the associated listed items. The term “or” as used herein, is to be interpreted as a mathematical OR, i.e., as an inclusive disjunction; not as a mathematical exclusive OR (XOR), unless expressly stated otherwise. In addition, the singular forms "a", "an" and "the" are to be interpreted as “at least one”, thus also possibly comprising a plurality of entities of the same kind, unless expressly stated otherwise. It will be further understood that the terms "includes", "comprises", "including" and / or "comprising", specifies the presence of stated features, actions, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, actions, integers, steps, operations, elements, components, and / or groups thereof. A single unit such as e.g. a processor may fulfil the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. A computer program may be stored/ distributed on a suitable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms such as via Internet or other wired or wireless communication system.

Claims

PATENT CLAIMS

1. A method (400) for planning and adapting transportation route (110) of a vehicle (100), driving from a starting point (210) to a destination (220), which method (400) comprises: obtaining (401) information related to starting point (210), and destination (220) of the vehicle (100); obtaining (403) a target arrival time of the vehicle (100), for arriving to the destination (220); determining (404) a number of candidate routes (110, 111) from the starting point (210) to the destination (220); collecting (407) information affecting transportation time of the vehicle (100) while driving along the candidate routes (110, 111), comprising: historical traffic data, from a first data source (140a); and live traffic data, from a second data source (140b); wherein the historical traffic data and the live traffic data are given a respective weight on different road sections (110a, 110b, 110c, 11 Od, 110e) of the route (110, 111), depending on a distance between the vehicle (100) and the road section (110a, 110b, 110c, 110d, 110e) ; calculating (408) required transportation time of the vehicle (100), while driving along the respective candidate routes (110, 111), based on the obtained (401) information and the collected (407) information; selecting (409) transportation route (110) out of the determined (404) candidate routes (110, 111), based on the made calculation (408); determining (410) a starting time for the vehicle (100), for departing from the starting point (210) towards the destination (220) along the selected (409) transportation route (110); and outputting (412) information to a driver of the vehicle (100) concerning the selected (409) transportation route (110) and the determined (410) starting time.

2. The method (400) according to claim 1, further comprising to repeatedly: determining (405) geographical position of the vehicle (100); collecting (407) information affecting transportation time of the vehicle (100); and calculating (408) required transportation time of the vehicle (100), based on the collected (407) information, and updated respective weights for the historical traffic data and the live traffic data.

3. The method (400) according to any one of claim 1 or claim 2, further comprising: obtaining (402) tachygraph data of the driver, from a tachygraph (310) in the vehicle (100); wherein the calculation (408) comprises determining an appropriate moment for a break for the driver, based on the obtained (402) tachygraph data; and wherein information concerning the determined breaks is output (412) to the driver of the vehicle (100).

4. The method (400) according to any one of claims 1 -3, wherein the calculation (408) of the required transportation time of the vehicle (100) comprises determining an appropriate speed of the vehicle (100), based on the collected (407) information; and wherein the information outputted (412) to the driver of the vehicle (100) comprises the determined appropriate speed.

5. The method (400) according to any one of claims 1-4, further comprising: obtaining (406) information related to an attribute of the vehicle (100) comprising any of: geometry of the vehicle (100), weight per axle, type of vehicle (100), remaining fuel in the tank of the vehicle (100), hazardous material in the cargo, spoilable cargo goods, fuel level of the vehicle (100), urea level of the vehicle (100); and wherein the calculation (408) of the required transportation time; and the selection (409) of transportation route (110) are based on the obtained (406) information.

6. The method (400) according to any one of claims 1-5, wherein: the collected (407) information affecting transportation time of the vehicle (100) further comprises metrological data, from a third data source (140c); and wherein the calculation (408) of the required transportation time of the vehicle (100) is adjusted based on the collected (407) metrological data.

7. The method (400) according to any one of claims 1-6, wherein: the calculation (408) of the required transportation time of the vehicle (100), the determination of appropriate speed of the vehicle (100), and the determination (410) of the starting time for the vehicle (100) are performed based on road grade of the route (110) and vehicle weight.

8. The method (400) according to any one of claims 1-7, wherein: the obtained (403) target arrival time of the vehicle (100) comprises information concerning cost for late arrival; and wherein the calculation (408) of the required transportation time of the vehicle (100), and the determination (410) of the starting time for the vehicle (100) is made based on the obtained (403) cost for late arrival.

9. The method (400) according to any one of claims 1-8, wherein the selection (409) of the transportation route (110) is made for optimisation with regard to any one of: shortest transportation time, lowest combined fuel and urea consumption, shortest working time of the driver, or lowest risk for an accident.

10. The method (400) according to any one of claims 1-9, wherein: the collected (407) information affecting transportation time of the vehicle (100) further comprises traffic influencing event data, from a fourth data source (140d); and wherein the calculation (408) of the required transportation time of the vehicle (100) is adjusted based on the collected (407) traffic influencing event data.

11. The method (400) according to any one of claims 1-10, further comprising: adjusting (411) speed for a cruise control unit (350) of the vehicle (100) to keep, according to the made calculations (408).

12. A computer program comprising instructions which, when the computer program is executed by a computer, cause the computer to carry out the steps of the method (400) according to any one of the preceding claims.

13. A control arrangement (130) for planning and adapting transportation route (110) of a vehicle (100), driving from a starting point (210) to a destination (220), which control arrangement (130) is configured to: obtain information related to starting point (210), and destination (220) of the vehicle (100), via a communication device (135); obtain a target arrival time of the vehicle (100), for arriving to the destination (220), via a communication device (135); determine a number of candidate routes (110, 111) from the starting point (210) to the destination (220); collect information affecting transportation time of the vehicle (100) while driving along the candidate routes (110, 111), comprising: historical traffic data, from a first data source (140a); and live traffic data, from a second data source (140b), wherein the historical traffic data and the live traffic data are given a respective weight on different road sections (110a, 110b, 110c, 11 Od, 110e) of the route (110, 111), depending on a distance between the vehicle (100) and the road section (110a, 110b, 110c, 110d, 110e) ; calculate required transportation time of the vehicle (100), while driving along the respective candidate routes (110, 111), based on the obtained information and the collected information; select transportation route (110) out of the determined candidate routes (110, 111), based on the made calculation; determine a starting time for the vehicle (100), for departing from the starting point (210) towards the destination (220) along the selected transportation route (110); and output information to a driver of the vehicle (100) concerning the selected transportation route (110) and the determined (410) starting time via an output device (340).

14. A system (500) for planning and adapting transportation route (110) of a vehicle (100), driving from a starting point (210) to a destination (220), comprising: a control arrangement (130), according to claim 13; a communication device (135), configured to communicate with a communication device (120) of the vehicle (100); a data source (140a, 140b, 140c, 140d), comprising information affecting transportation time of the vehicle (100) while driving along the candidate routes (110, 111) to the destination (220); and an output device (340) configured to output information to a driver of the vehicle (100).