Patent ID: 12221120

DETAILED DESCRIPTION

Those skilled in the art will appreciate that the steps, services and functions explained herein may be implemented using individual hardware circuitry, using software functioning in conjunction with a programmed microprocessor or general purpose computer, using one or more Application Specific Integrated Circuits (ASICs) and/or using one or more Digital Signal Processors (DSPs). It will also be appreciated that when the present invention is described in terms of a method, it may also be embodied in one or more processors and one or more memories coupled to the one or more processors, wherein the one or more memories store one or more programs that perform the steps, services and functions disclosed herein when executed by the one or more processors.

In the following description of exemplary embodiments, the same reference numerals denote the same or similar components.

The full software (SW) stack of an ADS may be considered as a complex mechanism. The application SW contains a multitude of components including several highly non-linear components such as filters and neural networks. In addition, the application SW is running on top of the platform base SW and hardware (HW), each of which may exhibit different types of errors and faults that may result in unwanted behaviour and failures. Stated differently, when unwanted and unexpected failures occur there is a vast number of potential error sources ranging from bad timing, electrical faults, bit flips, unexpected code behaviour, unexpected sensing performance deviations, unexpected environmental conditions, etc. Thus, it is desirable to be able to provide a system that performs safely, with the aforementioned conditions in mind, and not exposing anyone (occupants or other road users) to unreasonable risk.

In other words, it is desirable to have a simple, but powerful methods to supervise the output of the full SW stacks of an ADS that are capable of potentially catching previously undetected internal deficiencies/errors/problems in the nearly infinite space of discrete states that the system has.

Accordingly, the present inventors realized that a way to monitor the operation of the full SW and HW stack of the ADS is to monitor intended and measured (or otherwise obtained) parameters associated with the maneuvering of the vehicle at a specific location, and to compare these with corresponding parameters derived from a statistical model indicative of the (historical) behaviour of other vehicles at those locations. Thereby, one is able to determine whether there is any unusual or otherwise deviating decisions being taken by the ADS, which may be used as an indication of the performance of the ADS.

For example, assuming that the monitored parameter is a steering angle, and that there is a situation where the ADS of a vehicle decides to instruct the vehicle's control system to apply a steering angle of 4 degrees. Then, by comparing with the statistical model indicative of the behaviour of other corresponding vehicles at the same location (and with the same path intention, e.g., stay in the right-most lane) one may conclude that a steering angle of 4 degrees for that “intended path” is unusual behaviour since the statistical model indicates that the most common operation is a steering angle of −2 degrees. This conclusion, i.e., that the ADS's decision is a statistical “anomaly”, can effectively be used to identify potential safety-critical errors somewhere in the ADS SW/HW stack, or to identify “scenarios” (i.e., edge cases) that are of interest for development purposes.

Thus, if the intended or measured (or otherwise obtained) steering angle deviates too much (above a threshold) from the “norm”, one or possibly a plurality of “things” are not as the ADS expects them to be. Since the general object for any unsupervised vehicle is to be in “control” of any given situation, it is undesirable to have situations when the ADS appears unsure about the current state of the vehicle or of the surrounding environment.

Moreover, by using statistical models (preferably compiled from data obtained from a relatively homogenous fleet of vehicles) one does not need to stipulate what “correct” behaviour is in every situation. Instead, the “correct” behaviour is defined through statistical inference of how other vehicles, having the same “path intention”, have performed the corresponding tasks successfully at specific locations. Moreover, the utilization of statistical models further adds the benefit of having a dynamic reference frame, which adapts to real-world changes.

It also opens up for further analysis of the statistical model. For example, if there are locations with high variance in ADS behaviour, it could be used as an indication that it is a point of interest. It could, due to various reasons, potentially be the ADS that has problems in achieving consistent performance in this location, wherefore one could take the appropriate measures (e.g., SW update) to ensure a more consistent user experience.

Herein the term “monitoring operations of an automated driving system, ADS, of a vehicle” may comprise and/or refer to monitoring the ADS/performance of the ADS/operation of the ADS, which may comprise interpreting information, processing data/information and generating output for execution by the (vehicle/ADS) platform”.

Herein the term “obtaining”, in connection to e.g., “obtaining one or more parameters associated with performing a driving manoeuvre of a vehicle from a determined geographical position” may refer to terms comprising “acquiring”, “retrieving”, “receiving”, “determining”, “measuring”, “detecting”, “collecting”, “observing”.

Herein the term “intended path”, may be any suitable path for a vehicle with an automated driving system, ADS. The term “intended path”, may be understood as an indication of how the ADS is going to control the movement of the vehicle relative to a road geometry over a certain stretch of road, said stretch having any suitable distance and any suitable extension, where the road geometry may have one or more lanes.

Herein the term “steering angle”, may refer to any angle associated with steering the vehicle in connection to maneuvering the vehicle along an intended path according to the present disclosure. The term “steering angle”, may refer to the steering wheel angle of the steering wheel of the vehicle. The term “steering angle”, may refer to the angle of the ground engaging wheels of the vehicle relative to the longitudinal extension of the vehicle. The term “steering angle”, may refer to the angle of portions such as shafts/axles comprised in a mechanical arrangement connected to and arranged for steering the ground engaging steerable wheels of the vehicle relative to the longitudinal extension of the vehicle.

For the method and device for monitoring operations of an automated driving system (ADS) of a vehicle according to the present disclosure, a statistical model is utilized from which data is retrieved, indicative of a statistical distribution related to one or more intended and/or obtained parameters for an intended path corresponding to intended and obtained, e.g., measured, parameters associated with performing a driving manoeuvre of a vehicle from a determined geographical position along that intended path. According to an aspect of the present disclosure, said one or more intended and obtained parameters comprises intended and obtained steering angles for the vehicle for said intended path and/or intended and obtained position of said vehicle within a lane of said intended path.

Thus, for different specific paths/intended paths, such statistical models have according to an aspect of the present disclosure been obtained by means of collecting, from selected vehicles, one or more intended and obtained parameters associated with performing a driving manoeuvre of a vehicle from a determined geographical position along the intended path. Said selected vehicles may be from a fleet of vehicles having at least similar properties related to said one or more parameters associated with performing the driving manoeuvre of the vehicle from the determined geographical position along the intended path. Such statistical models obtained for different specific paths/intended paths are thus location specific, i.e., associated with specific paths/intended paths.

Such a statistical model, from which data indicative of a statistical distribution related to one or more intended and/or obtained parameters for an intended path is retrievable, is according to an aspect of the present disclosure configured to be arranged in an ADS-provided vehicle configured to perform a driving manoeuvre from a determined geographical position along the intended path.

Such a statistical model, from which data indicative of a statistical distribution related to one or more intended and/or obtained parameters for an intended path is retrievable, is according to an aspect of the present disclosure configured to be remotely operably connectable to an ADS-provided vehicle configured to perform a driving manoeuvre from a determined geographical position along the intended path.

Thus, such a statistical model according to the present disclosure may be arranged in or be remotely operably connectable to an ADS-provided vehicle. According to an aspect of the present disclosure, the statistical model according to the present disclosure is locally stored in a suitable memory device of the vehicle.

Herein the term “statistical model”, may be understood as a description of what are statistically reasonable parameter values across the intended path. In particular, the statistical model may indicate of how the ADSs of other vehicles in the fleet of vehicles, and where applicable non-ADS vehicles configured to facilitate providing relevant information/parameters, have executed paths in specific geographical areas in the past, in terms of intended and obtained (e.g., measured) parameters associated with the path execution manoeuvres.

In more detail, in connection to monitoring operations of an ADS of a vehicle, the statistical model comprises data indicative of a statistical distribution related to one or more intended and/or obtained parameters for an intended path along which said vehicle is configured to perform a driving manoeuvre. According to an aspect of the present disclosure, a set of data associated with parameters, e.g., steering angle and/or lane positioning, for performing a driving manoeuvre along a certain path has been modelled by means of different segmentation and quantification methods. Thus, a statistical model may be understood as a mathematical representation of a statistical distribution. In more detail, the statistical model of one or more parameters for performing a driving manoeuvre along a certain path quantifies what the ADS can expect from such an operation in a statistical way.

FIG.1is a schematic top view illustration of a vehicle V performing a driving manoeuvre along an intended path P in relation to embodiments of the present disclosure. The intended path P in the exemplary embodiment is a stretch of a lane L1of a road R. The road in this example has three lanes L1, L2, L3, a first lane L1along which the vehicle V is travelling, a second lane along which another vehicle V1is travelling and a third lane L3. The intended path P has a starting position P1and an end position P2. The intended path P has a distance D. The vehicle V is equipped with an automated driving system, ADS. The intended path P is thus a certain stretch of a certain road R, said stretch having a certain distance D and certain extension (e.g., straight, curved, etc.) The operation of the ADS of the vehicle is configured to be monitored by means of a device for monitoring operations of an ADS of a vehicle, see e.g.,FIG.4.

When the vehicle V was located at the starting position P1, that geographical position was determined by means of a control circuitry of the device of the vehicle, see e.g.,FIG.4. The geographical position, here the starting position P1, may be determined by means of the control circuitry in any suitable way, e.g., by means of a GNSS arrangement (e.g., GPS, GLONASS, BeiDou, Galileo, or any other regional variant thereof) of the vehicle together with map data, which may be comprised in said device or operably connectable to said device.

The intended path was also determined/obtained by means of said control circuitry of the device of the vehicle. The intended path may be determined by means of the control circuitry in any suitable way, e.g., by means of a path planning system/feature/function of the ADS, a GNSS-arrangement of the vehicle, and map data, which may be comprised in said device or operably connectable to said device.

When or prior to the vehicle V was located at the starting position P1for performing driving manoeuvre along the intended path P, one or more intended parameters, e.g., steering angle and/or lane positioning, associated with performing the driving manoeuvre of said vehicle from the determined geographical position P1along the intended path are determined. The one or more intended parameters, e.g., steering angle and/or lane positioning, may be determined by means of the control circuitry in any suitable way, e.g., by deriving the intended parameters from the determined intended path. However, the intended parameters may be retrieved or received from a path planning module/feature of the ADS. In more detail, the ADS may comprise a “path planner”, i.e., a path planning module that is configured to generate candidate paths for execution by a control module of the ADS platform, based on various input data (sensor data, map data, localization data, goal position, and so forth) as readily understood by the skilled person in the art.

When performing the driving manoeuvre along the intended path, one or more obtained parameters, e.g., steering angle and/or lane positioning, associated with performing the driving manoeuvre of said vehicle from the determined geographical position along the intended path are determined. The one or more intended parameters, e.g., steering angle and/or lane positioning, may be obtained by means of the control circuitry in any suitable way, e.g., by measuring said parameters, e.g., steering angle and/or lane positioning, by means of one or more vehicle-mounted sensors. Thus, the control circuitry may be configured to retrieve sensor data from the one or more vehicle-mounted sensor in order to obtain the one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position.

Further, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path are retrieved from a statistical model. The one or more corresponding intended and/or obtained parameters, e.g., steering angle and/or lane positioning, may be retrieved by means of the control circuitry from a suitable local or remote data repository. According to an aspect of the present disclosure, the one or more corresponding intended and/or obtained parameters, e.g., steering angle and/or lane positioning, may have been retrieved from said statistical model prior to performing said driving manoeuvre along the intended path. According to an alternative aspect of the present disclosure, the one or more corresponding intended and/or obtained parameters, e.g., steering angle and/or lane positioning, may be retrieved from said statistical model when performing said driving manoeuvre along the intended path.

Furthermore, for each monitored operation of the vehicle, it may be determined, by means of the control circuitry, based on said retrieved data, said determined intended path and at least one of the obtained parameters and the determined intended parameters, whether there is an anomaly associated with said monitored operation, and if so, take at least one action of a set of predefined actions. Said at least one action may depend on category of anomaly and may comprise performing a vehicle speed reduction; a hand-over procedure; a minimum risk manoeuvre; and/or, a selection of a driving policy out of a plurality of driving policies.

Such a vehicle V is explained in more detail below with reference toFIG.4.

FIG.2is a flow-chart representation of a method M1for monitoring operations of an automated driving system, ADS, of a vehicle in accordance with an embodiment of the present disclosure. According to an aspect of the present disclosure, the method M1comprises a number of steps configured to be performed for each monitored operation.

According to the aspect the method M1comprises a step S1. In this step, a geographical position of the vehicle is determined.

According to the aspect the method M1comprises a step S2. In this step, an intended path of the vehicle is determined.

According to the aspect the method M1comprises a step S3. In this step, one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path is/are determined. According to an aspect of said method, said one or more intended and obtained parameters comprises intended and obtained steering angles for the vehicle for said intended path. According to an aspect of said method, said one or more intended and obtained parameters comprises intended and obtained positions of said vehicle within a lane of said intended path.

According to the aspect the method M1comprises a step S4. In this step, one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position are obtained.

According to the aspect the method M1comprises a step S5. In this step, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path are retrieved.

According to the aspect the method M1comprises a step S6. In this step, it is determined, based on said retrieved data, said determined intended path and at least one of the obtained parameters and the determined intended parameters, whether there is an anomaly associated with said monitored operation.

According to the aspect the method M1comprises a step S7. In this step, if an anomaly is determined, at least one action of a set of predefined actions is taken.

The method M1for monitoring operations of an ADS of a vehicle is according to an embodiment adapted to be performed by the device described below with reference toFIG.4.

The method M1for monitoring operations of an ADS of a vehicle is according to an embodiment adapted to be performed by a computer program comprising computer-readable instructions which, when executed by at least one processor of a device for monitoring operations of an automated driving system, ADS, of a vehicle, causes the at least one processor to perform said method M1.

FIG.3is a flow-chart representation of a method M2for monitoring operations of an automated driving system, ADS, of a vehicle in accordance with an embodiment of the present disclosure. According to an aspect of the present disclosure, the method M2comprises a number of steps configured to be performed for each monitored operation. According to an aspect of the present disclosure, the method M2is a more specified embodiment of the method M1described above with reference toFIG.2. According to an aspect of the present disclosure, the steps S11, S12, S13, S14and S15described below, essentially correspond to the steps S1, S2, S3, S4, S5described above for the method M1with reference toFIG.2.

According to the aspect the method M2comprises a step S11. In this step, a geographical position of the vehicle is determined.

According to the aspect the method M2comprises a step S12. In this step, an intended path of the vehicle is determined.

According to the aspect the method M2comprises a step S13. In this step, one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path is/are determined. According to an aspect of said method, said one or more intended and obtained parameters comprises intended and obtained steering angles for the vehicle for said intended path. According to an aspect of said method, said one or more intended and obtained parameters comprises intended and obtained positions of said vehicle within one or more lanes of said intended path.

According to the aspect the method M2comprises a step S14. In this step, one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position are obtained.

According to the aspect the method M2comprises a step S15. In this step, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path are retrieved.

According to the aspect the method M2comprises a step S16A. In this step, said obtained parameters are compared with said retrieved data associated with corresponding obtained parameters for a corresponding intended path.

According to the aspect the method M2comprises a step S17A. In this step, it is determined that there is an anomaly if the difference between compared obtained parameters exceeds a predetermined threshold value.

According to the aspect the method M2comprises a step S16B. In this step, said determined intended parameters are compared with said retrieved data associated with corresponding intended parameters for a corresponding intended path.

According to the aspect the method M2comprises a step S17B. In this step, it is determined that there is an anomaly if the difference between compared intended parameters exceeds a predetermined threshold value.

According to the aspect the method M2comprises a step S16C. In this step, said determined intended parameters are compared with said retrieved data associated with corresponding obtained, e.g., measured, parameters associated with performing the driving manoeuvre for a corresponding intended path.

According to the aspect the method M2comprises a step S17C. In this step, it is determined that there is an anomaly if the difference between compared intended parameters and obtained parameters exceeds a predetermined threshold value.

The method M2may be configured to perform steps S16A, S17A and/or steps S16B, S17B and/or steps S16C, S17C.

According to an aspect of the present disclosure, the method M2is configured to determine that there is no anomaly if the difference between said compared parameters does not exceed a predetermined value.

According to the aspect the method M2comprises a step S18. In this step, when it is determined that there is an anomaly due to the difference between compared parameters exceeding a predetermined threshold value, the anomaly is categorized based on said comparison.

According to the aspect the method M2comprises a step S19. In this step, at least one action is taken, comprising generating a control signal indicating anomaly and category of anomaly; and performing, based on category of anomaly, at least one of: a vehicle speed reduction; a hand-over procedure; a minimum risk manoeuvre; and a selection of a driving policy out of a plurality of driving policies.

According to the aspect the present disclosure, the method M2comprises a step S20A. In this step, sensor data obtained from a set of vehicle mounted sensors stored during a time period is transmitted if it is determined that there is an anomaly, wherein the transmitted sensor data is from a time period around a point in time when the anomaly was determined.

According to the aspect the present disclosure, the method M2comprises storing, during a time period, sensor data obtained from a set of vehicle-mounted sensors.

According to the aspect the present disclosure, the method M2comprises a step S20B. In this step, ADS data obtained from the ADS of the vehicle stored during a time period is transmitted if it is determined that there is an anomaly, wherein the transmitted ADS data is from a time period around a point in time when the anomaly was determined.

According to the aspect the present disclosure, the method M2comprises storing, during a time period, ADS data obtained from the ADS of the vehicle.

The method M2for monitoring operations of an ADS of a vehicle is according to an embodiment adapted to be performed by the device described below with reference toFIG.4.

The method M2for monitoring operations of an ADS of a vehicle is according to an embodiment adapted to be performed by a computer program comprising computer-readable instructions which, when executed by at least one processor of a device for monitoring operations of an automated driving system, ADS, of a vehicle, causes the at least one processor to perform said method M2.

FIG.4is a schematic side view illustration of a vehicle V in accordance with an embodiment of the present disclosure.

The vehicle V comprises a perception system6and a localization system5. A perception system6is in the present context to be understood as a system responsible for acquiring raw sensor data from on sensors6a,6b,6c,6dsuch as cameras, LIDARs and RADARs, ultrasonic sensors, and converting this raw data into scene understanding. In particular, the vehicle V has at least one vehicle-mounted camera6cfor capturing images of at least a portion of a surrounding environment of the vehicle. The localization system5is configured to monitor a geographical position and heading of the vehicle and may in the form of a Global Navigation Satellite System (GNSS), such as a GPS. However, the localization system may alternatively be realized as a Real Time Kinematics (RTK) GPS in order to improve accuracy. Moreover, in the present context the vehicle V is assumed to have access to a digital map, e.g., a HD-map, either in the form of a locally stored digital map or via a remote data repository accessible via an external communication network20, e.g., as a data stream. In some embodiments, the access to the digital map may for example be provided by the localization system5. The vehicle V may comprise an antenna8for facilitating the connection to the external communication network20.

Further, the vehicle V may be connected to external network(s)20via for instance a wireless link, e.g., for retrieving map data. The same or some other wireless link may be used to communicate with other vehicles in the vicinity of the vehicle or with local infrastructure elements. Cellular communication technologies may be used for long range communication such as to external networks and if the cellular communication technology used have low latency it may also be used for communication between vehicles, vehicle to vehicle (V2V), and/or vehicle to infrastructure, V2X. Examples of cellular radio technologies are GSM, GPRS, EDGE, LTE, 5G, 5G NR, and so on, also including future cellular solutions. However, in some solutions mid to short range communication technologies are used such as Wireless Local Area (LAN), e.g., IEEE 802.11 based solutions. ETSI is working on cellular standards for vehicle communication and for instance 5G is considered as a suitable solution due to the low latency and efficient handling of high bandwidths and communication channels.

Said vehicle V comprises a device10for monitoring operations of an automated driving system, ADS, of said vehicle V.

The device10comprises a control circuitry11. Said control circuitry11comprises or is constituted by one or more processors. The control circuitry11may be also be referred to as a control circuit11, control unit11, or controller11.

The device10further comprises a memory12, a sensor interface13and a communication interface14. The device10preferably comprises a number of software/hardware modules here generalized as “control circuitry”11. The control circuitry11is configured to execute instructions stored in the memory12to perform a method for monitoring operations of an automated driving system, ADS, of a vehicle V according to any one of the embodiments disclosed herein. Stated differently, the memory12of the device10can include one or more (non-transitory) computer-readable storage mediums, for storing computer-executable instructions, which, when executed by one or more computer processors11, for example, can cause the computer processors11to perform the techniques described herein. The memory12optionally includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.

In more detail, the control circuitry11is configured to, for each monitored operation: determine a geographical position of the vehicle V; determine an intended path of the vehicle V; and, determine one or more intended parameters associated with performing a driving manoeuvre of said vehicle V from the determined geographical position along the intended path. The control circuitry11is further configured to, for each monitored operation: obtain one or more parameters associated with performing the driving manoeuvre of said vehicle V from said determined geographical position; and, retrieve, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. The control circuitry11is further configured to, for each monitored operation: determine, based on said retrieved data, said determined intended path and at least one of the obtained parameters and the determined intended parameters, whether there is an anomaly associated with said monitored operation; and, if an anomaly is determined, take at least one action of a set of predefined actions.

The present invention has been presented above with reference to specific embodiments. However, other embodiments than the above described are possible and within the scope of the invention. Different method steps than those described above, performing the method by hardware or software, may be provided within the scope of the invention. Thus, according to an exemplary embodiment, there is provided a non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a vehicle control system, the one or more programs comprising instructions for performing the method according to any one of the above-discussed embodiments. Alternatively, according to another exemplary embodiment a cloud computing system can be configured to perform any of the methods presented herein. The cloud computing system may comprise distributed cloud computing resources that jointly perform the methods presented herein under control of one or more computer program products.

Generally speaking, a computer-accessible medium may include any tangible or non-transitory storage media or memory media such as electronic, magnetic, or optical media—e.g., disk or CD/DVD-ROM coupled to computer system via bus. The terms “tangible” and “non-transitory,” as used herein, are intended to describe a computer-readable storage medium (or “memory”) excluding propagating electromagnetic signals but, are not intended to otherwise limit the type of physical computer-readable storage device that is encompassed by the phrase computer-readable medium or memory. For instance, the terms “non-transitory computer-readable medium” or “tangible memory” are intended to encompass types of storage devices that do not necessarily store information permanently, including for example, random access memory (RAM). Program instructions and data stored on a tangible computer-accessible storage medium in non-transitory form may further be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, which may be conveyed via a communication medium such as a network and/or a wireless link.

The control circuitry11, e.g., processor(s)11, associated with the device10for monitoring operations of the ADS of the vehicle V, may be or include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory12. The device10has an associated memory12, and the memory12may be one or more devices for storing data and/or computer code for completing or facilitating the various methods described in the present description. The memory may include volatile memory or non-volatile memory. The memory12may include database components, object code components, script components, or any other type of information structure for supporting the various activities of the present description. According to an exemplary embodiment, any distributed or local memory device may be utilized with the systems and methods of this description. According to an exemplary embodiment the memory12is communicably connected to the control circuitry11, e.g., via a circuit or any other wired, wireless, or network connection, and includes computer code for executing one or more processes described herein.

It should be appreciated that the communication interface14may also provide the possibility to acquire sensor data directly or via dedicated sensor control circuitry in the vehicle. The communication/antenna interface14may further provide the possibility to send output to a remote location, e.g., remote operator or control centre, by means of the antenna8. Moreover, some sensors in the vehicle may communicate with the device10using a local network setup, such as CAN bus, I2C, Ethernet, optical fibres, and so on. The communication interface14may be arranged to communicate with other control functions of the vehicle and may thus be seen as control interface also; however, a separate control interface (not shown) may be provided. Local communication within the vehicle may also be of a wireless type with protocols such as WiFi, LoRa, Zigbee, Bluetooth, or similar mid/short range technologies.

Accordingly, it should be understood that parts of the described solution may be implemented either in the vehicle, in a system located external the vehicle, or in a combination of internal and external the vehicle; for instance in a server in communication with the vehicle, a so called cloud solution. For instance, sensor data may be sent to an external system and that system performs the steps to compare the sensor data (movement of the other vehicle) with the predefined behaviour model. The different features and steps of the embodiments may be combined in other combinations than those described.

It should be noted that the word “comprising” does not exclude the presence of other elements or steps than those listed and the words “a” or “an” preceding an element do not exclude the presence of a plurality of such elements. It should further be noted that any reference signs do not limit the scope of the claims, that the invention may be at least in part implemented by means of both hardware and software, and that several “means” or “units” may be represented by the same item of hardware.

Although the figures may show a specific order of method steps, the order of the steps may differ from what is depicted. In addition, two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the invention. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps. The above mentioned and described embodiments are only given as examples and should not be limiting to the present invention. Other solutions, uses, objectives, and functions within the scope of the invention as claimed in the below described patent embodiments should be apparent for the person skilled in the art.