Patent Description:
An objective of road signs may typically be to instruct or inform a user of a vehicle of the traffic rules or a traffic instruction at an area. A road sign is often a picture that is describing the instructions that is valid at the area and the user of a vehicle learns the meaning of a road sign when taking the driving license or similar.

Understanding traffic instructions of road signs in foreign countries can be difficult and even more difficult if the road sign comprise text in a foreign language that the user don't know. Understanding the instructions of combinations of road signs can also be hard in some cases. Prior art traffic sign assist systems are focused on translating the text identified on the road signs and translate the meaning of the text on the road signs. Even the most sophisticated traffic sign assist systems have a problem of interpreting the instructions of a road sign unless the user or the system understands the language the text is written in.

Understanding traffic instructions of combination of road signs are even more difficult.

In areas where the driver of a vehicle is unfamiliar with the road signs and the instructions given, there is thus a need for alternative approaches to assist the driver to understand the traffic rules or instructions by predicting or estimating the traffic instructions of a road sign.

The document <CIT> discloses a method for automatic identification of parking spaces and non parking spaces.

It should be emphasized that the term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, integers, steps, or components, but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

An object of some embodiments is to provide alternative approaches to the traffic instructions/rules that are valid for an area.

An object of some embodiments is to provide alternative approaches to understand instructions on road signs to understand the traffic instructions/rules that are valid for an area.

According to a first aspect, this is achieved by a method for estimating a traffic instruction at a first area.

The method comprises detecting positions and/or movements of at least one vehicle in a second area associated with a corresponding traffic instruction as the first area.

The method further comprises determining a behaviour pattern based on the detected positions and/or movements of the at least one vehicle. The behaviour pattern is estimated based on how long and when the at least one vehicle is standing still in the second area.

The method further comprises estimating the traffic instructions of the first area based on the determined behaviour pattern.

An advantage of some embodiments is an alternative approach to understanding the traffic instructions of the first area is provided.

An advantage of some embodiments is that information from different areas, i.e. first and second areas, with the same traffic instruction could be crowdsourced to estimating the traffic instruction. An advantage of this is that the traffic instructions for areas having a low amount/number of moving vehicles could be estimated if there is another area with the corresponding traffic instruction.

Another advantage of some embodiments is that the estimation of the traffic instruction of the area is based on how actual vehicles move in the second area and not of an interpretation or a translation of a road sign.

Yet an advantage of some embodiments is that the estimation of the traffic instruction is provided even for environments with road signs that give no meaning, even if the text as such is interpreted or translated to an understandable language. There are road signs that are hard to understand, as they are local signs.

Yet another advantage of some embodiments is a method that is non-dependent of translating the text of the road sign is provided.

By corresponding traffic instruction is meant that similar or the same traffic instructions are valid for the different areas.

According to an aspect the method comprises the step of receiving the estimated traffic instructions of the first area in a further vehicle entering the first area. An advantage of this is that a user of a vehicle entering the first area obtains information regarding the traffic instructions for the first area without any need of understanding the road signs at the first area.

According to an aspect the step of detecting comprises scanning the second area by one or more sensors.

According to an aspect the first and/or second area is a parking area. According to an aspect the area is an area that corresponds to an area for which road signs is used to define the traffic instructions.

According to an aspect the step of determining comprises analysing the detected positions and/or movements of the at least one vehicle in the cloud.

According to an aspect the step of estimating the traffic instructions of the first area comprises the step of analysing the determined behaviour pattern in the cloud.

By determining and analysing information in the cloud the advantages of that the ability to handle large amounts of information does not need to be processes in every vehicle.

According to an aspect the method further comprises detecting one or more of a road sign, a road, traffic lanes, markings on the road and the step of estimating the traffic instructions is further based on the detected one or more road signs, road, traffic lanes and/or signs on the road. An advantage of the aspect is that the information provides more data for identifying patterns in the areas.

According to an aspect the method further comprises the step of displaying the estimated traffic instruction of the first area in the further vehicle. An advantage of some aspects is that the driving of the vehicle may be adapted according to the estimated traffic instructions the area without understanding any road signs as such.

According to an aspect the method further comprises the step of receiving the estimated traffic instruction of the first area in an autonomous driving system of the further vehicle.

According to an aspect the estimated traffic instructions of the first area comprises a parking instruction to assist the user of the further vehicle or the autonomous driving system to obey the traffic instructions of the first area.

According to an aspect the scanning of the area comprises scanning one or more of parked vehicles in the second area. An advantage of the aspect is that the information provides data for training the modelling of the traffic conditions and competing flows of traffic in identifying behaviour patterns to estimate meaning of many different types of traffic instructions at the first and/or second areas.

According to an aspect, the detecting and/or scanning comprises any one of detecting and/or scanning as the vehicle approaches or enters the second area, as the vehicle passes the second area, and as the vehicle has passed the second area.

An advantage of some embodiments is that the detecting and/or scanning provides data at different points in time and at different positions in the second area for further training the modelling of the traffic instructions, the traffic conditions and competing flows of traffic and identify behaviour pattern(s).

According to an aspect the method further comprises sending a continuous stream of detected and/or scanned information to the cloud in response to the detection and/or scanning of the second area.

An advantage of some aspects is that the continuous information provides a non-interrupted stream of data for a more correct training of the modelling/estimating of the traffic instructions at the first area and traffic conditions and competing flows of traffic of the first and/or second areas.

According to an aspect the estimated traffic instruction of the first area comprises a parking instruction to assist the user of the vehicle to obey the traffic rules at the first area.

An advantage of some aspect is that the driving of the further vehicle may be adapted to the traffic instructions of the firs area and thereby obey the traffic instructions in the first area.

According to an aspect the method comprises analysing the road sign information in the cloud and wherein the step of estimating the traffic instruction of the first area is further based on the analysis of the road sign information. An advantage of some aspects is that the estimation of the traffic instructions could be a combination of data from the detecting and/or scanning and the road signs to even further increase the accuracy of the estimation of the traffic instructions for the area.

A second aspect is a computer program product comprising a non-transitory computer readable medium, having thereon a computer program comprising program instructions. The computer program is loadable into a data processing unit and configured to cause execution of the method according to the first aspect when the computer program is run by the data processing unit.

A third aspect is an apparatus for estimating the traffic instructions at the area.

The apparatus comprises a memory comprising executable instructions and one or more processors configured to communicate with the memory.

The one or more processors are configured to cause the apparatus to detect positions and/or movements of at least one vehicle in the first area or in a second area associated with a corresponding traffic instruction as the first area.

The one or more processors are further configured to cause the apparatus to determine a behaviour pattern based on the detected positions and/or movements of the at least one vehicle, and estimate the traffic instructions of the first area based on the determined behaviour pattern.

An advantage of some embodiments and aspect is an alternative approach to understanding the traffic instructions in the first area are provided
Another advantage of some embodiments is that the estimation of the traffic instruction of the first area is based on how actual vehicles move in the second area.

Yet an advantage of some embodiments is that an estimated traffic instruction is provided even for a first area with road signs that give no meaning, even if the text as such is translated to an understandable language. There are road signs that are hard to understand, as they are local signs.

Yet another advantage of some embodiments is that a non-dependence of translating the text of the road sign is provided.

A fourth aspect is a vehicle comprising the apparatus of the third aspect.

An advantage of some embodiments is that alternative approaches to understanding the traffic instructions at an area are provided.

A fifth aspect is a cloud service for estimating a traffic instruction at the area.

The cloud service comprises controlling circuitry configured to receive detected positions and/or movements of at least one vehicle in a second area associated with a corresponding traffic instruction as the first area, determining a behaviour pattern based on the detected positions and/or movements of the at least one vehicle, and estimating the traffic instructions of the first area based on the determined behaviour pattern. An advantage of some embodiments is that an alternative approach to understanding the traffic instructions for an area are provided.

Another advantage of some embodiments is that the estimate of the traffic instruction of the area is based on how actual vehicles move in the area and not on a translation of a road sign.

Yet an advantage of some embodiments is that an estimated traffic instruction is provided even for environments with road signs that give no meaning, even if the text as such is translated to an understandable language. There are road signs that are hard to understand, as they are local signs.

According to an aspect the controlling circuitry of the cloud service is further configured to: provide a recommended velocity, driving direction, a parking instruction, non-driving direction, a maximum height, maximum weight, bridge opening, pedestrian street, bus lane, carpool lane, no-passing zone, tolls, speeding camera, and specific rules for specific lanes to a user of a vehicle or an autonomous driving vehicle to assist to obey the traffic instructions of the first area.

A sixth aspect is a system for estimating traffic instructions of a first area.

The system comprises a scanning module configured to scan a second area, a transmitting module configured to send a continuous stream of scanning information, an analysis module configured to analyse the scanning information, a estimating module configured to predict an traffic instructions at the first area based on the analysis of the scanning information, a receiving module configured to receive the estimated instruction of the first area, and a display module configured to display the estimated traffic instruction of the first area.

An advantage of some embodiments is that alternative approaches to understanding the traffic instructions of the first area are provided.

Another advantage of some embodiments is that the estimation of the traffic instruction of the first area is based on how actual vehicles move in the second area.

Yet an advantage of some embodiments is that an estimate of the traffic instruction is provided even for environments with road signs that give no meaning, even if the text as such is translated to an understandable language. There are road signs that are hard to understand, as they are local signs.

Yet another advantage of some embodiments is that a non-dependence of translating text of any road sign is provided.

According to an aspect the receiving module is further configured to receive one or more of a recommended velocity, driving direction, parking instructions, and non-driving direction to assist the user of the vehicle to obey the traffic rules.

As already mentioned above, it should be emphasized that the term "comprises/comprising" when used in this specification is taken to specify the presence of stated features, integers, steps, or components, but does not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof.

Embodiments and aspects of the present disclosure will be described and exemplified more fully hereinafter with reference to the accompanying drawings.

In the following, embodiments and aspect will be described where alternative approaches to interpret, predict or estimate the traffic instructions of an area is provided. Further, embodiments and aspect will be described where alternative approaches to interpret, predict or estimate the traffic instructions of an area based on analysing movements of vehicles and persons in an area having the same or similar traffic instructions.

<FIG> is a flowchart illustrating example method steps according to an aspect. The method <NUM> is a method for estimating a traffic instruction at a first area <NUM>. Thus, the method <NUM> may, for example, be performed by the system <NUM> of <FIG> for estimating a traffic instruction of the first area <NUM>. By estimating is meant to determine, predict, understand, i.e. to try to understand the rules or traffic instruction of the area. According to an aspect the first area is one or more of a parking area, an intersection, a road section, a city or part of a city. According to an aspect the first area is an area that corresponds to an area for which road signs is used to define the traffic instructions. According to an aspect the first area is an area that corresponds to an area for which a specific traffic instruction is valid.

The method <NUM> for estimating an instruction of an area <NUM> comprises following steps.

Detecting S1 positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in a second area <NUM> associated with a corresponding traffic instruction as the first area <NUM>. By position is according to an aspect meant the location or position of the at least one vehicle <NUM> within the first or second area <NUM>, <NUM> and/or the time that it has been positioned in the position. By position is according to an aspect meant the location or position of the at least vehicle user <NUM> within second area <NUM>, <NUM> and/or the time that it has been positioned in the position. By vehicle user <NUM> is meant a person that in some way is associated with the vehicle, it could be a driver or a passenger. By movement is according to an aspect meant the direction, speed and/or distance that the at least one vehicle <NUM> moves within the first or second area <NUM>, <NUM>.

The first area <NUM> is the area for which the traffic instruction should be estimated. The second area <NUM> is another area that has the same/similar/corresponding traffic instruction as the first area <NUM>. The second area <NUM> can be located at a distance from and be remote from the first area <NUM>. According to an aspect the first and second areas <NUM>, <NUM> is a parking area, a block, a zone with a specific radius, a street, a part of a city or a city. According to an aspect the first and second area s <NUM>, <NUM> are different for different traffic instructions.

According to an aspect the step of detecting S1 comprises scanning S11 the second area <NUM> by one or more sensors <NUM>. According to an aspect the scanning S11 further comprises scanning S11 by on-board sensors <NUM> of the vehicle <NUM> and/or a sensor <NUM> mounted at the second area <NUM>.

According to an aspect, the vehicle <NUM> is capable of Vehicle to Cloud (V2C) communication. The V2C technology enables an exchange of information of the vehicle and a further vehicle <NUM> or information obtained by the vehicle <NUM> with a cloud system or a cloud service <NUM>. This allows the further vehicle <NUM> to use information from vehicles <NUM> that is also capable of V2C communication, trough the common cloud system or cloud service.

The on-board sensors <NUM> may comprise detectors, cameras, <NUM>-degree radar, LIDAR, ultrasonic sensors or any other vehicle compatible sensor for obtaining information about the environment in proximity of the vehicle <NUM>.

In addition, the vehicle <NUM> and or further vehicle <NUM> may comprise an Internet connection, Advanced Driver Assistance Systems and/or high definition maps for more accurate object detection and localization.

The sensor <NUM> is according to an aspect sensors <NUM> that are mounted at the second area <NUM>. According to an aspect the sensors <NUM> are a cloud connected sensor <NUM>. The sensor <NUM> is according to an aspect a camera that is fixedly connected at the second area <NUM>, <NUM>. According to an aspect the sensor <NUM> is a camera located at the second area <NUM>, <NUM> being an intersection or parking place. The sensor <NUM> could for instance be a surveillance camera at a parking area or a sensor at building in the area <NUM>. The sensors <NUM> at the first and/or second area may comprise detectors, cameras, <NUM>-degree radar, ultrasonic sensors or any other sensor for obtaining information about the environment in proximity of the first and/or second areas.

According to an aspect the detected movements comprises information regarding one or more of time, date, speed of vehicles and/or vehicle users, position of vehicles and/or vehicle users, number of vehicles, type of vehicle and direction of vehicles and or vehicle users.

According to an aspect the detected movements comprises information regarding one or more of the width, height, type, model and type of tires or the vehicles.

In step S2 a behaviour pattern is determined S2 based on the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM>.

According to an aspect the step of determine S2 may be performed by big data analytics. Big data analytics is often a complex process of examining large and varied data sets, i.e., big data, to uncover information including hidden patterns, unknown correlations, trends and preferences for making informed decisions or predictions. According to an aspect the big data analytics is performed in a scalable cloud system or cloud service.

In an aspect the step of determining S2 the behaviour pattern based on the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM> is determined by a software analysis patterns in software engineering being conceptual models, which capture an abstraction of a situation often encountered in modelling. A behaviour pattern can be represented as a group of related, generic objects (e.g. meta-classes) with stereotypical attributes (e.g. data definitions), behaviours (e.g. method signatures), and expected interactions defined in a domain-neutral manner. By determining and analysing behaviour patterns in the detected information, patterns can be identified and the estimation of the traffic instruction can be based on the identified pattern.

The position of vehicles is according to an aspect meant the relative position between the vehicles <NUM>. The position of vehicles is according to an aspect meant the position of the vehicles on a road. The position of vehicles is according to an aspect meant the position of the vehicles on a parking spot or parking area. The type of vehicle is according to an aspect the size of the vehicle. By knowing the size of the vehicle, the type of vehicle could be identified as a cycle, a motorcycle, a car, a bus, a permobil, a snowmobile and/or a truck. The type of vehicle is according to an aspect the function of a vehicle such as a taxi, an ambulance, a police car, a post or a delivery vehicle. The second area <NUM> is used to detect more movements for a traffic instruction, such that there are more data available for the step of determining S2. By having more data that are available the determining S2 of behaviour data could be more accurate and also be determined for areas, i.e. the first area <NUM>, for which yet not enough data has been detected to determine a behaviour pattern.

In step S3, a traffic instruction of the first area <NUM> is estimated based on the determined behaviour pattern determined in step S2.

In step S4, the estimated traffic instructions of the first area <NUM> is received in the further vehicle <NUM>. The receiving may according to an aspect be performed by a receiving circuitry configured for V2C comprised in the further vehicle. The further vehicle <NUM> according to an aspect receives the traffic instructions of the first area <NUM> as the further vehicle <NUM> is entering or approaching the first area <NUM>, such that an autonomous driving system or driver of the further vehicle <NUM> obtains/receives information of the estimated traffic instructions and can obey them. According to an aspect the vehicle <NUM> is a vehicle that is positioned in the first or second area <NUM>,<NUM> and that detects movements. According to an aspect the further vehicle <NUM> is a vehicle that should use the estimated traffic instructions. According to an aspect the further vehicle <NUM> is a vehicle <NUM>.

In some embodiments and aspects, the method <NUM> for estimating an instruction of the area further comprises the following step.

According to an aspect the step of determining S2 comprises analysing S21 the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM> in the cloud <NUM>. By analysing the information in the cloud <NUM> the information could be gathered and analysed in an effective way and each vehicle does not need to have the data power to make the analysis.

According to an aspect the step of estimating S3 the traffic instructions of the first area <NUM> comprises the step of analysing S31 the determined behaviour pattern in the cloud <NUM>. By analysing the information in the cloud <NUM> the information could be gathered and analysed in an effective way and each vehicle does not need to have the data power to make the analysis.

According to an aspect the vehicle <NUM> is a vehicle that is positioned in the second area <NUM> and that detects movements. According to an aspect the further vehicle <NUM> is a vehicle that should use the estimated traffic instruction.

Detecting S5 one or more of a road sign, a road, traffic lanes, markings on the road and the step of estimating S3 the traffic instructions is further based on the detected one or more road signs, road, traffic lanes and/or signs on the road. The detected additional information is used as a further input when estimating S3 the traffic instruction. This could reduce the time for estimating or the data power needed. If for instance a road sign is identified as something that has to do with parking instructions, the estimation S3 could focus the estimation on vehicles standing still in the first and/or second area and vehicle users that are moving to and from their vehicles. In another example and if the road sign is identified as regarding a instruction of how the vehicle can move, the estimation S3 could focus on behaviour patterns of moving vehicles. The information of the road sign could be used as input in the analysis of the instructions at the area. If the road sign is identified as being traffic information related to parking information for the area, the step of estimating S3 could start and focus on how long and when vehicles is standing still in the area and spend less analysing power on the speed and direction that vehicles is having in the area. However, if the road sign is identified as being traffic information related to the speed and the direction that the vehicle is allowed to drive, the estimation S3 could start and focus on the speed and direction of vehicles in the second area <NUM> and spend less analysing power on vehicles standing still in the second area <NUM>.

This could be used to make the estimation S3 more efficient. This could also be used in areas <NUM> for which the cloud <NUM> has not yet received enough information to make a complete analysis of the first and/ or second areas <NUM>, <NUM>.

According to an aspect the method <NUM> comprises the further step of displaying S6 the estimated traffic instruction of the first area <NUM> in the further vehicle <NUM>.

According to an aspect the method <NUM> comprises the further step of receiving S7 the estimated traffic instruction of the first area <NUM> in an autonomous driving system of the further vehicle <NUM>.

According to an aspect the estimated traffic instructions of the first area <NUM> comprises instructions of one or more of a recommended velocity, a driving direction, a parking instructions, a non driving direction, a allowed type of tyres, a maximum height, maximum weight, bridge opening, pedestrian street, a lane for autonomous driving vehicles, bus lane, carpool lane, no-passing zone, tolls, speeding camera, specific rules for specific lanes to assist the user of the vehicle or the autonomous driving system to obey the traffic instructions of the first area <NUM>.

According to an aspect the method <NUM> comprises the further step of identifying S8 a first indication/indicator of the traffic instruction associated with the first area <NUM>. According to an aspect the step of identifying S8 the first indication comprises identifying one or more road signs and/or signs on and/or at the road at the first area <NUM>.

According to an aspect the method <NUM> comprises the further step of comparing S9 the first indication with a second indication of the traffic instruction associated with the second area <NUM> to determine S10 if the first and second indications are indications of corresponding traffic instructions.

According to an aspect, a continuous stream of the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM> is sent to the cloud <NUM> in response to the step of detecting S2 of the area. The continuous stream of detected information may form part of big data to be analysed by a cloud service.

According to an aspect the step of displaying S6 is done by displaying the traffic instruction on a dashboard, in a head-up display or in a Human Machine Interface in the vehicle.

According to an aspect the detecting S1 of movements and/or positions of vehicle user comprise detecting information of the flow of pedestrians in the second area <NUM>.

According to an aspect, an example of the traffic instruction may be that it is allowed to park on a road on weekdays from <NUM> to <NUM> and that there is a parking fee. Another example of a traffic instruction is that the speed limit is <NUM>/h and that not is allowed to stop the vehicle on weekends.

According to an aspect a behaviour pattern in the second area <NUM> of a road is that the average speed of the vehicles on the road is <NUM>/h, and one estimation based on this could be that the traffic instruction of the road, i.e. of the first area <NUM> and/or second area <NUM>, is that the speed limit is <NUM>/h in the first area <NUM>.

According to an aspect not part of the claimed invention, the behaviour pattern of the first area <NUM> of a parking place is that the vehicles that stop in an area has an average time of more than <NUM> minutes from when it stops until it is locked and that almost no vehicle is standing still for more than <NUM>. An estimation of the traffic instruction of the parking place in this area <NUM> is based on this pattern and could be that the allowed time to park is <NUM> and that there is a parking fee.

According to an aspect a behaviour pattern is that there are almost only taxis driving in the second area <NUM> in weekends. An estimation of the traffic instruction of the first and second areas <NUM>, <NUM> based on this behaviour pattern could be that the only taxis are allowed on the road on weekends.

According to an aspect the estimated traffic instruction is any instruction that is or could be disclosed on a road sign <NUM>.

<FIG> is a schematic overview illustrating an example system according to some aspects and embodiments. The system <NUM> is for estimating the traffic instructions at the first area <NUM>. Thus, the system <NUM> may, for example, be utilized for the first area <NUM> being an environment 300a of <FIG> and/or for the first area <NUM> being an environment 300b of <FIG>.

The vehicle <NUM> comprises an apparatus for estimating traffic instructions of the first area <NUM>. The apparatus comprises a memory comprising executable instructions, wherein one or more processors are configured to communicate with the memory.

The one or more processors are configured to cause the apparatus to detect S1 positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in a second area <NUM> associated with a corresponding traffic instruction as the first area <NUM>.

The one or more processors are further configured to cause the apparatus to determining S2 a behaviour pattern based on the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM>.

The one or more processors are further configured to cause the apparatus to estimating S3 the traffic instructions of the first area <NUM> based on the determined behaviour pattern.

The vehicle <NUM> scans, i.e., detects, the environment in proximity of the vehicle <NUM>, e.g. lane markings, other vehicles in other lanes or in the same lane, pedestrians, cyclists, road signs <NUM>, the intersection, the traffic lights and their current states using the on-board sensors which may comprise e.g. detectors, cameras, <NUM>-degree radar, LIDAR, ultrasonic sensors or any other vehicle compatible sensor for obtaining information about the environment in proximity of the vehicle.

The vehicle <NUM> according to an aspect comprises a cloud connection or an Internet connection, Advanced Driver Assistance Systems and high definition maps for more accurate object detection and localization.

The vehicle <NUM> according to an aspect send a continuous stream of detected positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in the second area <NUM> obtained by the on-board sensors, as described above, to the cloud <NUM> for determining and estimation.

The sensors <NUM> mounted at the area scans, i.e. detects, the environment in proximity of the second area <NUM>, e.g. lane markings, vehicles in different lanes pedestrians, cyclists, road signs <NUM>, the intersection, the traffic lights and their current states. The sensor <NUM> may comprise e.g. detectors, cameras, <NUM>-degree radar, ultrasonic sensors or any other compatible sensor for obtaining information about the environment in proximity of the area.

The sensor <NUM> at the second area <NUM> may send a continuous stream of information obtained by the sensors, as described above, to the cloud <NUM> for determination and estimation.

The cloud <NUM> comprises according to an aspect at least one cloud database <NUM> and/or at least one server database <NUM> which are databases that typically run on a cloud computing platform and access to it is provided as a service. The database services may provide scalability and high availability of the database. The cloud <NUM> may be comprised in a one separate cloud service <NUM> or in a plurality of associated cloud services <NUM>.

In the cloud <NUM>, machine learning algorithms create a model capable of estimating the traffic instructions for the area, e.g. an instruction, based on input parameters, i.e., detected positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in the second area <NUM>, such as where other vehicles are detected in relation to the vehicle <NUM> and how many other vehicles there are, which vehicles are currently driving and at which speed and direction, a road sign, the type of vehicle, the time between parking and locking the vehicle, time of day, pedestrians at crosswalks (waiting or walking) and how many pedestrians there are etc..

The cloud <NUM> can then provide the vehicle <NUM> or the further vehicle <NUM> approaching or entering the first area <NUM> with the estimated traffic instructions of the first area <NUM>, so that the further vehicle <NUM> is able to adapt to obey the traffic instructions for the first area <NUM>. According to an aspect the traffic instructions could be any traffic instructions that is disclosed by a road sign. According to an aspect the instruction is one or more of a speed limit, a driving direction, a parking instructions, a non-driving direction, a maximum height, a maximum weight, a bridge opening, a maximum parking time, environmental zone, the type of tires allowed, pedestrian street, bus lane, a carpool lane, no-passing zone, tolls, speeding camera, specific rules for specific lanes to assist the user of the vehicle to obey the traffic rules. According to an aspect the instructions could be any instruction that is valid for the first area <NUM> and that could be identified by analyzing the positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in the first area <NUM> or in the second area <NUM>.

The cloud <NUM> receives a continuous stream of detected information <NUM> of an environment, e.g. the second area <NUM>, obtained from the on-board sensors from the vehicle <NUM> and/or from the sensors <NUM> at the second area <NUM>.

The cloud <NUM> analyses the scanning information in response to reception of the stream of detected positions and/or movements. The cloud <NUM> predicts/estimated the traffic instructions of the first area <NUM> based on the determined behaviour pattern of the detected information in the second area <NUM>.

The cloud <NUM> may predict the traffic instructions of the first area <NUM> based on the scanning information obtained from the vehicle <NUM> and from scanning information obtained by other vehicles of which scanning information may be comprised in the databases <NUM>,<NUM>. The cloud <NUM> may predict the traffic instructions of the area <NUM> based on the scanning information obtained from the one or more sensors <NUM> detecting information. The cloud <NUM> may estimate the traffic instructions of the first area <NUM> based on the detected information obtained from the vehicle <NUM> and from scanning information obtained by other vehicles and the sensors <NUM> mounted at the area of which scanning information may be comprised in the databases <NUM>,<NUM>. According to an aspect the cloud <NUM> sends a stream of information comprising the estimated traffic instruction of the area <NUM> to the further vehicle <NUM>, as is disclosed in <FIG> and <FIG>.

More specifically, according to an aspect the determining of the information is performed by a machine learning algorithm, which creates a model of the first area <NUM> and the second area <NUM>, which is continuously improved, i.e. trained, as long as new information becomes available. Until the model is fully trained, a more rudimentary model could be used. An example of this may be that the method is capable of estimating the traffic instructions based on a combination of identifying known road signs in combination with input from the vehicle <NUM> and/or the cloud connected sensors <NUM> mounted at the second area <NUM>. A rudimentary model in this case is to just assume that is OK to park in an area where there is a parking sign, even though it might lead to a mistake if there are additional information on the sign that yet not can be predicted. This information can for instance be used to roughly estimate a recommended vehicle speed, where to stop etc. for other cloud connected vehicles. The time for the model to be fully trained for the first area <NUM> is however reduced as the model in addition to information from the first area <NUM> is based on information from the second area <NUM>.

The method provides the estimated traffic instructions of the first area <NUM> to the further vehicle <NUM> as it is approaching or entering the first area <NUM>.

In some embodiments, the further vehicle <NUM> may further receive a recommended velocity, driving direction, parking instructions, non-driving direction, a maximum height, maximum weight, bridge opening, pedestrian street, bus lane, HOV (carpool) lane, no-passing zone, tolls, speeding camera, specific rules for specific lanes to assist the user of the further vehicle <NUM> or to assist an autonomous driving system of the further vehicle <NUM> to obey the traffic rules.

In some embodiments, the further vehicle <NUM> may further display the predicted instruction to assist the further vehicle <NUM> to obey the traffic rules at the first area of the one or more road signs <NUM>.

<FIG> is a schematic drawing illustrating an example environment according to some embodiments. The road sign instruction prediction system <NUM> illustrated in <FIG> may, for example, be utilized for an environment 300a of <FIG>.

<FIG> illustrates two environments, i.e. the first and the second areas <NUM>, <NUM>, comprising a plurality of vehicles <NUM> driving on a road towards the first or second area <NUM>, <NUM> with sensors <NUM> and with a road signs <NUM>. In this example, the environments each comprises a road that passes a parking area. The first and the second areas <NUM>, <NUM> does not need to be exactly the same, however, some of the traffic instructions for the first area <NUM> should be the same as some of the traffic instructions in the second area <NUM>. In the two environments in <FIG> the traffic instructions regarding how and when it is allowed to park in the parking lot in the first area <NUM> is the same as the traffic instructions regarding how and when it is allowed to park in the parking lot in the second area <NUM>. According to an aspect the corresponding traffic instructions regarding the parking could be identified by that the parking signs <NUM> are the same.

The further vehicle <NUM> approaching or entering the first area <NUM> receives or obtains an estimation of the traffic instructions of the first area <NUM>. According to an aspect the instructions is an estimated traffic instruction of where it is allowed to park and where it is not allowed to park. According to an aspect this could be estimated based on where different vehicles stands still and for how long they stand still.

According to an aspect the estimated instruction comprise instructions of if there are any fees for parking, such that the further vehicle <NUM> does not violate the parking rules of the first area. According to an aspect this could be predicted based on the time from which the vehicle is stopped until it is locked, i.e. that it takes time for the user of the vehicle to walk to the parking meter, pay the fees and walk back. According to an aspect this could be estimated based on if the vehicle uses a parking fee app or not.

According to an aspect the estimated instruction comprises instructions of for how long it is allowed to park at the parking area/lot, i.e. in the first area <NUM>. According to an aspect the estimated instruction comprises instructions of at which days of the week it is allowed to park. According to an aspect this could be estimated based on when different vehicles stands still, for how long they stands still and if there are differences in the pattern between different days of the week and month.

According to an aspect the estimated instructions comprises instructions of if one needs to have a special sticker or information in the car to fulfil the rules. The further vehicle <NUM> is further configured to display the received estimated instruction such that a user of the further vehicle <NUM> can see the traffic instructions.

According to an aspect the further vehicle <NUM> is further configured to input the received estimated instruction to a driving assisting system of the further vehicle <NUM>.

The estimated instructions of the one or more road signs may also comprises a mix of parking instructions and traffic instructions depending on the traffic instructions of the first area and the road signs that are present at the first area.

<FIG> is a schematic drawing illustrating an example environment according to some embodiments. The road sign instruction estimation system <NUM> illustrated in <FIG> may, for example, be utilized for an environment 300b of <FIG>.

<FIG> illustrates two environment, i.e. the first and the second areas <NUM>,<NUM>, comprising a plurality of vehicles <NUM> driving towards/through/leaving the environments, i.e. the first or second areas <NUM>,<NUM>, with one or more road signs <NUM> in different directions. In this example, the first or second area <NUM>, <NUM> comprises an intersection of two roads. The first area <NUM> does not need to be exactly the same as the second area <NUM>, however, some of the traffic instructions in the first area <NUM> should be the same as some of the traffic instructions in the second area <NUM>. In the illustrated environment the traffic instructions regarding how it is allowed to turn in the intersections is the same in the first and the second areas <NUM>,<NUM>. The corresponding traffic instructions could according to an aspect be identified by that the same road signs <NUM> are located at the intersection and/or that the same markings <NUM> are located in the road. Information from the first and second areas <NUM>, <NUM> regarding traffic instructions that not is the same in both the areas <NUM>, <NUM> could be used as input when estimating a traffic instruction in a further are that has the same traffic instruction. The first or second area <NUM>, <NUM> comprises two sensors <NUM> mounted at the first and the second area <NUM>, <NUM>. The sensors <NUM> could be mounted on a lamppost, a parking meter, a road sign or a building at the area.

When the vehicle <NUM> approaches the first area <NUM> or the second area <NUM> comprising the intersection, the vehicle <NUM> scans, i.e., monitors, the state of the traffic light for each direction, senses or approximates the number of other vehicles in its proximity with respect to the intersection, senses the speed and direction of the vehicles, senses the type of vehicle and senses any pedestrians next to the road or crossing the road, including their position.

The sensor(s) <NUM> at the first and second areas <NUM>, <NUM> of the intersection, also scans the respective areas <NUM>, <NUM>, i.e., monitors, the state of the traffic lights for each direction, senses or approximates the number of other vehicles in its proximity with respect to the intersection, senses the speed and direction of the vehicles, senses the type of vehicle and senses any pedestrians next to the road or crossing the road, including their position.

Further, the vehicles <NUM> and/or sensors <NUM> mounted at the area may sense pictures and or text on the road or on the road signs <NUM>, indicating e.g. a lane direction, or any other relevant signs for modelling a traffic pattern at the area at one or more road signs, e.g. yield signs such as OK to turn at red etc..

The vehicle <NUM> according to an aspect continuously sends the detected information to the cloud <NUM>. The vehicle <NUM> may alternatively send the scanning information to the cloud <NUM> at specifically determined points in time or at regular time intervals e.g. each <NUM> seconds.

The cloud connected sensors <NUM> according to an aspect continuously send the scanning information to the cloud <NUM>. The sensors <NUM> may alternatively send the scanning information to the cloud <NUM> at specifically determined points in time or at regular time intervals e.g. each <NUM> seconds.

When the vehicle <NUM> reaches the intersection with the road signs <NUM> and when driving through the first or second area <NUM>,<NUM>, the vehicle <NUM> continuously senses or approximate the number of other vehicles, and their locations at the intersection as well as other vehicles driving through the area including their position, direction and vehicle speed. Also all visible road signs <NUM> and traffic lights are sensed around the vehicle <NUM>.

Further, the vehicle <NUM> identifies pedestrians next to the road or crossing the road, including their position.

When the vehicle <NUM> leaves the area, the vehicle <NUM> senses the number of oncoming vehicles i.e. vehicles that are about to enter the area, senses the traffic flows behind the vehicle (e.g. using rearward facing vision sensors).

The vehicle <NUM> according to an aspect continuously sends the scanning information to the cloud <NUM> until e.g. <NUM> meters after the intersection or until end of sight.

The further vehicle <NUM> approaching or entering the first area <NUM> receives or obtains the estimated traffic instructions of the first area <NUM> from the cloud <NUM> e.g. an instruction for the further vehicle <NUM> to follow such that further vehicle <NUM> obey the traffic rules at the first area <NUM>. According to an aspect the further vehicle <NUM> is configured to display the received estimated traffic instruction in the further vehicle such that a user of the further vehicle <NUM> obtains and could follow the traffic rules at the first area <NUM>. According to an aspect the further vehicle <NUM> are further configured to use the received estimated traffic instruction as input to an autonomous drive system or navigation system of the further vehicle <NUM>, such that the further vehicle <NUM> follows the traffic rules at the first area <NUM>.

The estimated traffic instructions according to an aspect comprises the speed limit in the main road and on the crossroads. According to an aspect this could be estimated based on the speed of the different vehicles.

According to an aspect the estimated instruction comprises information of which roads in the intersection that is it is allowed to drive into. This could for instance be predicted or estimated based on the traffic flow and if vehicles has turned into the different roads.

According to an aspect the estimated instructions comprises information of when and where it is allowed to stop and for how long it is allowed to stop.

According to an aspect the estimated instructions comprises information of that a lane is a taxi lane or a lane for a specific type of vehicle. According to an aspect this is based on a pattern of that only a specific type of vehicle is driving in a specific lane.

According to an aspect the different instructions for the area of the intersection is based on the pattern of vehicles that has passed the intersection.

According to an aspect the first area <NUM> is a continuous first area <NUM>. According to an aspect the first area <NUM> is a continuous first area <NUM> surrounding the further vehicle <NUM>.

<FIG> is a schematic block diagram illustrating an example arrangement according to some aspects. The example arrangement is a pattern detection arrangement <NUM> for detecting patterns in traffic flow behaviours of the second area <NUM> to estimate the traffic instructions of the first area <NUM>.

The pattern detection arrangement <NUM> comprises controlling circuitry CNTR <NUM>, which may in turn comprise a scanning arrangement SCAN <NUM>, e.g. scanning circuitry, configured to scan or monitor the second area <NUM>, an analysing arrangement ANLS <NUM>, e.g. analysing circuitry, configured to analyse the scanning information, an estimation arrangement PRED <NUM>, e.g. estimation circuitry, configured to estimate an instruction of the area, a receiving arrangement REC <NUM>, e.g. receiving circuitry, configured to receive the estimated instruction on the area, and a display arrangement DSPL <NUM>, e.g. display circuitry, configured to display the received estimated instructions.

The pattern detection arrangement <NUM> may be comprised in the pattern detection system <NUM> described in connection with <FIG> and/or the pattern detection arrangement <NUM> may be configured to perform method steps of any of the methods described in connection with <FIG>.

<FIG> is a schematic drawing illustrating an example computer readable medium according to some aspects. The computer program product comprises a non-transitory computer readable medium <NUM> having thereon a computer program <NUM> comprising program instructions, wherein the computer program being loadable into a data processing unit and configured to cause execution of the method steps of any of the methods described in connection with <FIG>.

According to some embodiments, the computer program product comprises a computer readable medium such as, for example a universal serial bus (USB) memory, a plug-in card, an embedded drive or a read only memory (ROM). <FIG> illustrates an example computer readable medium in the form of a compact disc (CD) ROM <NUM>. The computer readable medium has stored thereon a computer program comprising program instructions. The computer program is loadable into a data processor (PROC) <NUM>, which may, for example, be comprised in an apparatus or vehicle <NUM>,<NUM>. When loaded into the data processing unit, the computer program may be stored in a memory (MEM) <NUM> associated with or comprised in the data-processing unit. According to an aspect, the computer program may, when loaded into and run by the data processing unit, cause execution of method steps according to, for example, any of the methods illustrated in <FIG> or otherwise described herein.

According to an aspect, and as is disclosed in <FIG>, the cloud service <NUM> for estimating a traffic instruction of a first area <NUM> comprises controlling circuitry configured to: receive S100 detected S1 positions and/or movements of at least one vehicle <NUM> and/or vehicle user <NUM> in the second area <NUM> associated with a corresponding traffic instruction as the first area <NUM>, determining S2 a behaviour pattern based on the detected positions and/or movements of the at least one vehicle <NUM> and/or vehicle user <NUM>, and estimating S3 the traffic instructions of the first area <NUM> based on the determined behaviour pattern. According to an aspect the controlling circuitry of the cloud service is further configured to provide S101 a recommended velocity, driving direction, a parking instruction, non-driving direction, a maximum height, maximum weight, bridge opening, pedestrian street, bus lane, carpool lane, no-passing zone, tolls, speeding camera, and specific rules for specific lanes to a user of a vehicle or an autonomous driving vehicle to assist to obey the traffic instructions of the first area <NUM>. The described embodiments and aspect and their equivalents may be realized in software, hardware or a combination thereof. The embodiments may be performed by general purpose circuitry. Examples of general purpose circuitry include digital signal processors (DSP), central processing units (CPU), co-processor units, field programmable gate arrays (FPGA) and other programmable hardware. Alternatively or additionally, the embodiments may be performed by specialized circuitry, such as application specific integrated circuits (ASIC). The general purpose circuitry and/or the specialized circuitry may, for example, be associated with or comprised in an apparatus such as a vehicle.

Embodiments and aspects may appear within an electronic apparatus (associated with or comprised in a vehicle) comprising arrangements, circuitry, and/or logic according to any of the embodiments described herein. Alternatively or additionally, an electronic apparatus (associated with or comprised in a vehicle) may be configured to perform methods according to any of the embodiments described herein.

In the same manner, it should be noted that in the description of embodiments and aspect, the partition of functional blocks into particular units is by no means intended as limiting.

Claim 1:
A method (<NUM>) for estimating a traffic rule at a first area (<NUM>), wherein the first area (<NUM>) is a parking area, wherein the estimated traffic rule of the first area (<NUM>) comprises a parking instruction, the method comprising the steps of:
- detecting (S1) positions and/or movements of at least one vehicle (<NUM>) in a second area (<NUM>) associated with a corresponding traffic rule as the first area (<NUM>), wherein the second area (<NUM>) is a parking area, wherein the detected positions and/or movements comprises information regarding one or more of time, date and position of the at least one vehicle (<NUM>),
- determining (S2) a behaviour pattern based on the detected positions and/or movements of the at least one vehicle (<NUM>) in the second area (<NUM>), and
- estimating (S3) the parking instruction of the first area (<NUM>) based on the determined behaviour pattern, wherein the behaviour pattern is estimated based on how long and when the at least one vehicle (<NUM>) is standing still in the second area (<NUM>).