SYSTEM FOR MOTORCYCLE-RELATED THREATS

The project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825012.

The present invention generally relates to road safety, and specifically to methods and systems for warning road users, in particular vehicle drivers as traffic participants, of the presence of motorcycle-related threats. Other types of road users, such as pedestrians entering the road or other motorcycle-drivers, can benefit from the described method and system by an appropriate implementation of the present invention.

Motorcyclists may represent a serious issue if not properly addressed when driving in a highly dense highway road due to their high agility, acceleration and easiness of maneuver among bigger vehicles. Currently, the lanes structure is designed to accommodate medium/large-sized vehicles thus allowing the small-sized ones, i.e. motorcycles, to simultaneously share the lanes with larger vehicles. This results in serious safety issues as it may exacerbate the vehicles’ blind spot problem, that is, regular vehicle drivers might not be able to spot motorcyclists driving next to (or overtaking) the vehicle. Furthermore, drivers are more and more prone to distraction, which worsens their response time in an emergency situation.

In 2016, motorcyclist deaths on the US roads have been 28 times more frequent than deaths among car passengers and 35 times more frequent than deaths among light truck passengers (for reference, see National Highway Traffic Safety Administration, “2016 MOTORCYCLES Traffic Safety Fact Sheet", available at: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/ 812492). Those statistics clearly show the impelling need of methods and systems in charge of monitoring the traffic and mitigating this phenomenon.

A key-enabler of such methods and systems is a tracking component capable of inferring suitable information about the motorcyclists’ behaviors, e.g. position, speed, etc. While on four-wheeled vehicles this may easily be achieved by means of on-board tracking units, it is more difficult to install such dedicated units on motorcycles due to their smaller size. Moreover, motorcyclists may not be willing to install any on-board unit that may spoil the design of their motorcycles or their own driving pleasure.

While the topic is interesting and topical, only few works have looked into this road safety issue from a motorcyclist viewpoint. One of these works describes a system that tackles such a problem by means of a standalone solution called Motorcycle Warning System, MWS (for reference, see http://www.motorcyclewarningsystem.com/index.htm). This system requires an MWS unit installed on each motorcycle as well as on each regular vehicle. If a motorcycle is located within a certain distance range from another vehicle, the WMS unit equipped for that vehicle notifies the driver about the upcoming potential threat. This system has several disadvantages, which include that it requires the installation of dedicated hardware and that it cannot realistically be deployed in an efficient and scalable way.

It is therefore an object of the present invention to improve and further develop a method and a system for warning road users of the presence of motorcycle-related threats in such a way that without the need of dedicated motorcycle on-board tracking equipment a high reliability of warnings can be achieved in an efficient and scalable way.

In accordance with the invention, the aforementioned object is accomplished by a method comprising the features of claim 1. According to this claim, such a method comprises gathering information about a motorcycle, the information including at least a position of the motorcycle, using the information to determine a situation specific motorcycle awareness area, evaluating one or more potential safety threats associated with the motorcycle within the motorcycle awareness area, and depending on the evaluated potential safety threats, providing notifications to road users located within the motorcycle awareness area. Furthermore, the aforementioned object is accomplished a system comprising the features of claim 13. According to this claim, such a warning system comprises a mission control component that is configured to gather, via a cellular network infrastructure, information about a motorcycle, the information including at least a position of the motorcycle, to use the information to determine a situation specific motorcycle awareness area, to evaluate one or more potential safety threats associated with the motorcycle within the motorcycle awareness area, and, depending on the evaluated potential safety threats, to provide warning messages to road users located within the motorcycle awareness area.

According to embodiments of the invention it has been recognized that the above objective can be accomplished by automated methods and systems based on a cellular roadside network infrastructure, in particular a 5G infrastructure providing 5G-NR connectivity that monitors the motorcyclists’ behavior on the road and enables the deployment of a mission control that automatically sends alert messages in advance to vehicle drivers so as to avoid potential threats. As such, in contrast to various prior art solutions, embodiments of the invention exploit the roadside network infrastructure and can therefore be executed without any major effort from the driver side. The use of the roadside 5G network infrastructure to track motorcyclists, keep track of speed, motion patterns and inform the other drivers about upcoming possible threats allows a much more efficient deployment in the current road infrastructure. As a result, embodiments of the present invention provide an integrated warning system/method for improving road safety based on 5G-NR.

According to embodiments of the invention, all the motorcycles on the road are accurately tracked by means of the 5G-New Radio (5G-NR) connectivity with the roadside network infrastructure without any need of dedicated hardware, i.e. without relying on dedicated devices installed on the motorcycles. Actually, embodiments of the invention rely on the reasonably assumption that motorcyclists carry their own network device (typically a mobile device, such as a smartphone) when driving. Preferably, the network device (hereinafter sometimes briefly denoted UE, User Equipment) provides 5G-NR connectivity and may be synchronized with a network base station placed on the road infrastructure. Consequently, the techniques for tracking motorcyclists on the road may exploit the synchronization signals and the adaptive beamforming capabilities of 5G-NR on the roadside network infrastructure to infer the motorcyclists’ position, speed and trajectory without any need of dedicated hardware and employing a single 5G-NR base station.

In other words, a motorcyclist’s position may be gathered through the roadside network infrastructure and processed by a mission control component which derives his/her motion speed and trajectory. According to embodiments the current motorcyclist’s information (including, e.g., position, speed and trajectory) may be used together with past information to infer future motorcyclist’s information, for instance by applying machine learning, ML, techniques.

According to embodiments all the information acquired by tracking components may be transferred to a mission control component and exploited to forecast any upcoming dangers and possibly warn drivers of the involved vehicles. The mission control component may be located on the core network or edge network of the road infrastructure that can leverage such information to distinguish motorcyclists from other four-wheeled vehicles, classify their level of dangerousness, predict any upcoming safety threat and warn the involved vehicles’ drivers in time. According to embodiments the mission control component may be configured to notify all other vehicle drivers within a certain range through the roadside network infrastructure about any future possible threat due to motorcyclists’ presence.

There are several ways how to design and further develop the teaching of the present invention in an advantageous way. To this end it is to be referred to the dependent patent claims on the one hand and to the following explanation of preferred embodiments of the invention by way of example, illustrated by the figure on the other hand. In connection with the explanation of the preferred embodiments of the invention by the aid of the figure, generally preferred embodiments and further developments of the teaching will be explained. In the drawing the only

Fig. 1 is a schematic view illustrating a technique for vehicle localization in a 5G cellular network-based system according to an embodiment of the invention, Fig. 2 is a diagram illustrating the information flow in a warning system for road users according to an embodiment of the invention,

Fig. 3 is a schematic view illustrating a general motorcycle awareness scenario used in a warning method according to an embodiment of the invention,

Fig. 4 is a schematic view illustrating a motorcycle awareness scenario at an intersection used in a warning method according to an embodiment of the invention,

Fig. 5 is a schematic view illustrating a motorcycle awareness scenario in a mid corner hazard event used in a warning method according to an embodiment of the invention,

Fig. 6 is a diagram illustrating the activities of involved entities in a warning method according to embodiments of the invention,

Fig. 7 is a diagram illustrating the activities of involved entities in a warning method according to alternative embodiments of the invention.

Generally, in the following figures like numerals denote like components with the same or a similar functionality.

Currently, there is a lack of an up-and-running system that actively addresses motorcycle-related safety issues. Embodiments of the present invention address this shortcoming by exploiting an (already existing or upcoming) roadside cellular infrastructure, specifically by introducing a technique that is based on 5G-NR (New Radio) connectivity for tracking motorcycles’ positions.

In the context of cellular network- based localization techniques, it is assumed that a motorcyclist carries along a network device that provides the motorcyclist with a network connection to the mobile (5G) network. Typically, this network device will be a smart device such as a smartphone. Hereinafter, such a device, without limitation, will be briefly referred to as UE (User Equipment). Alternatively, the network device may be mounted on the motorcycle.

In order to enable accurate location measurements, it is further assumed that the UE is not idle during the motorcyclist’s driving time. Depending on the specific implementation, this assumption may correspond to the UE not entering RRC-IDLE (Radio Resource Control) operational mode. According to an embodiment this condition is achieved by employing RRC-IDLE suppression techniques. When the condition is met, it is possible to frequently update the motorcycle’s position (e.g., every few ms).

A particular profile and associated DRX (Discontinuous Reception) parameters may apply to the UE of a motorcyclist, which enables the device to remain connected in support of proper localization and track of the driver’s mobility pattern. This may result in an increased drainage of the UE’s battery, but increases the safety of the driver. Optionally, the driver’s UE may be plugged into a special holder, which on the one hand provides a safe place to store the UE while driving, and on the other hand connects the UE to the on-board power supply system.

According to embodiments of the invention, in order to accurately determine a motorcyclist’s current position, any of the following localization techniques may be employed. (An overview of these techniques together with further details can be found in K. Witrisal and C. Anton-Haro (Editors):“Whitepaper on New Localization Methods for 5G Wireless Systems and the Internet-of-Things”, COST Action CA 15104 (IRACON), 2018, available at: http://www.iracon.org/wp- content/uploads/2018/03/IRACON-WP2.pdf, which is incorporated herein by way of reference):

1. Multipath-assisted localization. According to this technique, by exploiting specular multipath components (MPCs), additional information about the UE’s position can be extracted from the radio signal. In the context of this technique, the above reference specifies: “Multipath-assisted localization exploits specular multipath components (MPCs) to obtain additional position information contained in radio signals [...]. It will help to overcome poor channel conditions like obstructed LOS and NLOS propagation situations and it gives diversity that is needed to improve the robustness in such cases.”

2. NLOS Identification and Mitigation: Machine Learning Approach. These techniques take into consideration that in ranging measurements an additional delay may be caused by NLoS (non-line-of-sight) conditions. By using ML techniques, there is no need of statistical modeling the LoS (line-of-sight) and NLoS channels in order to mitigate the NLoS effect. In the context of this technique, the above reference specifies:

“There are new and promising techniques for NLOS identification based on machine learning techniques. These techniques in the form of support vector machines, are optimization-based approaches that have been demonstrated to be effective in NLOS classification in both outdoor [...] and indoor environments [...]. In both approaches, the Least-squares Support Vector Machine (LSSVM) are used to perform both NLOS identification and mitigation, an approach that does not require any statistical modeling of LOS and NLOS channels, hence can performs both tasks under a common framework.”

3. Crowd-based learning approaches for NLOS mitigation. With this approach, a UE takes advantage of the available estimation of NLoS obtained by exploiting the measurements of the UEs that have already crossed the same area. In the context of this technique, the above reference specifies:

“The model mismatch in real-world situations can be accounted for by incorporating in the model unknown parameters, such as the extra delay due to NLOS conditions, that, in general, are functions of space and that have to be jointly estimated with the location. A way to tackle this problem is to make use of crowd-based learning approaches in which position-dependent unknown parameters are treated as spatial fields of which knowledge is refined by exploiting the presence of a large amount of users (the crowd) that enter the area of interest simultaneously or in different times. Specifically, after a user crosses the area, it takes advantage of the available estimated field obtained from measurements acquired by previous users. In turn, the estimate of this field is updated by the measurements of this user. Thereby, subsequent users can also benefit by using the field for their own localization.”

4. NLOS Identification and Mitigation: Classical Fingerprinting and Ray-tracing. This is an evolution of the existing LTE feature (for reference, see page 12, right col., section V.D.4) of the above reference).

As will be appreciated by those skilled in the art, the above techniques may be applied in any arbitrary combinations, depending on the specific application scenario (e.g. considering traffic density, operational resources, desired accuracy, etc.). In any case, above-mentioned techniques will increase the accuracy of ranging measures performed by a 5G-NR base station.

Fig. 1 schematically illustrates an embodiment of the invention in which a 5G-NR base station 100 performs ranging measurements (e.g., time of flight - ToF) of a target UE carried by a motorcyclist as well as directional measurements that aim at detecting the direction of arrival of signals from the target UE. After having collected ranging and directional measurements, the 5G-NR base station 100 may perform a geo-localization technique (for e.g. trilateration) and infers the exact position of the UE.

Specifically, the base station 100 (which the UE is attached to and which, using 5G terminology, will be briefly referred to as gNB hereinafter) may perform ranging and directional measurements of the motorcyclist’s UE during the 5G RACH procedure (in the initial UE connection process) or by decoding 5G NR Reference Signals (for e.g. DMRS, SRS). By means of adaptive beamforming, the gNB 100 may allocate a beam to each UE and steers it adaptively to track it. Therefore, the gNB 100 is aware of the Direction of Arrival (DoA) of each UE uplink signal. This information is sufficient to infer the exact position of the UE and track it along its motion path including the locations P1 , P2, and P3, as shown in Fig. 1. By cross-referencing the retrieved information with the road map, the gNB 100 is able to derive the UE’s (and thus the motorcyclist’s) motion path on the road. Moreover, in order to enhance the precision of the measurements, the variation of the signal-to-noise ratio (SNR) may be exploited as an additional feature, even though it is more sensitive to fading effects.

Fig. 2 schematically illustrates a centralized mission control 200 of a warning system for road users according to an embodiment of the invention. For instance, the mission control 200 may be a functional entity within an edge MEC (Mobile Edge Computing) server, or it may be implemented inside a datacenter. As another option, a gNB 100 with advanced processing capabilities can process the information from the UE and send it to the mission control 200 to derive context information.

According to the illustrated embodiment the mission control 200 comprises a data analysis module 210, mission control functions 220 and, as an optional enhancement, a ML (Machine Learning) engine 230. Generally, the centralized mission control 200 is configured to receive reports 240 from a gNB 100, wherein these reports 240 may include collected results of the location measurements performed by gNB 100, in particular positioning information as well as mobility pattern (e.g. acceleration, overtaking maneuvers, etc.) information about each vehicle. For four-wheeled vehicles these information may be retrieved via dedicated tracking units installed within the vehicles, so called OBUs (On-Board Units).

As shown in Fig. 2, the above information may be fed into the ML engine 230 to train one or more dedicated ML models. For instance, the models may include a ML model that aims at distinguishing a motorcycle from any other type of vehicles. Additionally or alternatively, the models may include a ML model that aims at determining and classifying a risk level of a motorcycle based on, for example, the speed variation, handover frequency and lane-change frequency. Further models may be trained that aim at predicting motorcycles’ future trajectories and/or at notifying other vehicles’ drivers within a specific notification-zone, namely awareness area, about the presence and characteristics of motorcycles and the distance/time of an encounter. The awareness area (i.e. its size, radius, shape, etc.) can vary depending on for e.g. the type of road, national regulations, type of motorbike, etc. Basically, as will be appreciated by those skilled in the art, state-of-the-art ML techniques could be used for the abovementioned purposes. As an example, the approach described in R. Amorim et al.: “Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements”, in 2017 IEEE Globecom Workshops (GC Wkshps), Singapore, 2017, pp. 1 -6, which is incorporated herein by way of reference, proves to be effective in identifying UAV-UEs based on LTE radio measurements, while a specifically tuned LSTM network may be used for predicting the UE’s next positions.

Finally, according to an embodiment the mission control 200 processes the information received from the gNBs 100 to predict trajectories of each vehicle (including motorbikes) by means of a deterministic algorithm applied by the data analytics module 210. Furthermore, for each detected motorcycle, the mission control 200 may send a notification information to all drivers located within a notification zone that is determined based on the characteristics of the respective motorcycle and time of encounter with the motorcycle.

Tracked location data may be processed to perform the classification, identification and prediction tasks per the above description in order to obtain information about the relevant road users and their characteristics. The mission control functions 220 may then exploit such information to create notification messages 250 and to distribute them to the relevant vehicles and their drivers. To some extends, the mission control functions 220 might even inform pedestrians walking close to the awareness area or even within the awareness area about upcoming dangerous motorcycles. This can be performed through the mobile device the pedestrian may be provided with.

The mission control functions 220 may be configured to also receive additional information 260 that is exposed by suitable other functions or sources than the gNBs 100 and to exploit such information to generate the notification messages. For instance, this may include information to determine cars in the proximity of an identified motorbike driver, as well as the cars’ direction, and/or environmental information (e.g., about the road condition and/or the traffic situation). According to an embodiment the mission control functions 220 may be further configured to create value added services, VAS, 270 and to provide them to the identified motorcyclist. It may be provided that the mission control functions 220 use, e.g., environmental information to push notifications to a particular motorcyclist that approaches a dangerous spot, e.g. a curve on a winding road with a non-visible construction behind. In this context, motorcyclists may be provided with virtual reality equipment, e.g., a wireless mobile- or on-board device that interacts with the driver to make the driver aware of the notifications from the mission control functions 220, for example through acoustic or visual signals or projections in the driver’s helmet.

Embodiments of the invention rely on cellular-based contextual information. According to these embodiments the mobile network infrastructure capabilities are exploited to discover a motorcyclist and to get its accurate and current position on the road. As already mentioned above, this reasonably assumes that the motorcyclist holds a mobile device (e.g. a mobile phone or any other smart device), which is connected to the mobile network infrastructure. Mobile devices of different motorcyclists connected to the network infrastructure may be assigned with different DRX (Discontinuous Reception) parameters that allows the network infrastructure to identify and track the respective motorcycles.

The roadside infrastructure of the mobile network may rely on edge computing capabilities (e.g. MEC platform services) to obtain the location of each motorcyclist within a certain accuracy, while still preserving his/her anonymity for privacy reasons. Once a motorcyclist is detected, a modification message may be sent to the mission control 200. The notification message may include detailed information about the detected motorcycle, such as its geographical position, motion speed, an identification of the motorcycle type, and/or an identification of the motorcycle itself.

Fig. 3 schematically illustrates a general motorcycle awareness scenario in which a warning system according to an embodiment of the invention may suitably applied. The warning system comprises the mission control 200 that has communication links 310 with the mobile network infrastructure. In Fig. 3 only three gNBs 100 deployed along a road 320 are exemplarily depicted as part of the mobile network infrastructure. The system is configured to gather the current position of each motorcyclist 330, to process its trajectory and speed and further estimate the potential safety threat within a given range, which is defined as a motorcycle awareness area 340. Specifically, the motorcycle’s position may continuously be tracked and sent to the mission control 200 by means of the cellular network as depicted in Fig. 3.

Based on a designated policy, the mission control 200 may be configured to perform different actions. For instance, it may be provided that the mission control 200 transmits a notification to other vehicles 350 driving in proximity of the motorcycle 330 within the motorcycle awareness area 340. Additionally or alternatively, it may be provided that the mission control 200, e.g. by appropriate notifications, prevents drivers of other vehicles 350 within the motorcycle awareness area 340 from overtaking or changing lanes when the motorcycle is approaching or will be approaching in the next few moments (for e.g. based on a time threshold to be properly tuned).

Further configurations include the transmission of a notification to vehicles 350 approaching an intersection 400 whenever motorcyclists 330 are within a potential collision path, as shown in the scenario of Fig. 4. In this case, the motorcycle awareness area 340 can be regarded as an intersection awareness area. Still further configurations include the transmission of a notification to vehicles 350 approaching a motorcycle 330 from the opposite direction to advise the vehicle driver to not overtake and to be aware of the approaching motorcycle 330 in order to avoid mid-corner hazards, where the driver of the motorcycle 330 (and possibly a pillion rider’s body) may exceed the lane markings and, hence, enter partially the vehicle’s 350 track, in particular in an area with winding roads, as shown in the scenario of Fig. 5.

In the context of the above configurations it should be noted that the determination of the positions of the respective vehicles 350 within a motorcycle awareness area 340 as well as the transmission of the mentioned notifications may be performed based on conventional communication via the vehicles’ 350 OBUs (On-Board Units). While the embodiments described above in connection with Figs. 3-5 allow to avoid unintentional collisions with motorcycles 330, the mission control component 200 needs to continuously collect data from the motorcycles 330 on the road 320. Such data can be obtained from contextual information with different levels of technology intrusiveness.

For instance, according to embodiments data about motorcycles may be collected without contextual information being available, for instance due to the fact that the motorcyclists may not be equipped with any smart device on board, i.e. no network connections are available. In such cases, the respective motorcycles may be are automatically recognized by means of cameras and sensors, such as proximity sensors or radars, deployed on several road-spots. Such road-spot devices, referred to as road-side units (RSU), may be connected to the roadside infrastructure of the mobile network infrastructure and they may be configured to allow sending a message to the mission control component as soon as a motorcycle is detected.

Fig. 6 is a diagram illustrating the activities of involved entities in a warning method according to embodiments of the invention, in which data about motorcycles on the road is collected either via mobile network devices (carried along by the motorcyclists or mounted on the motorcycle) or via RSUs as described above. The data model used in the activity diagram of Fig. 6 is the following:

In the scenario of Fig. 6 it is assumed that a base station, BS, 100 of a mobile network infrastructure detects a motorbike. In response to this detection, as shown at step 1., BS 100 sends a EXP_AWARENESS_INFO message to the mission control 200 with detailed information regarding the motorbike’s current position together with a motorbike ID and a motorbike type. This type is used by the mission control 200 to perform the motorbike awareness area calculation process, as different types of motorcycles might require different area ranges to monitor. As shown at step 2., the mission control 200 replies with a status code by sending EXP_AWARENESS_RESP message to the BS 100.

Generic vehicles on the road, which are equipped with a conventional OBU 600, circle through a loop in which the OBU 600 sends LOC_UPDATE_REQ messages to the BS 100 it is connected to, as shown at step 3a. Preferably, these messages are transmitted in regular time intervals as long as no other vehicle enters into an awareness area of the respective OBU 600. As shown at step 3b., in reaction to the receipt of LOC_UPDATE_REQ, BS 100 transmits a OBU _. LOCATIONJNFO to the mission control 200 in order to provide an update of the current location of the respective vehicle. Corresponding response messages are sent from the mission control 200 to the BS 100 (as shown at step 4a.) and from the BS 100 to the OBU 600 (as shown at step 4b.).

Based on the information received from the BS 100 about a detected motorcycle, the mission control 200 starts calculating an appropriate motorcycle awareness area. Together with the information from the OBUs 600 of generic vehicles, the mission control can determine those vehicles that are located within the motorcycle awareness area and can transmit appropriate warning messages to these vehicles via the BS 100, as shown at steps 5a./5b. and 6a./6b.

According to a specific embodiment a more advanced sensing technique may be deployed that infers not only the motion speed of each motorcyclist, but also the frequency of its lane changes, which are then periodically notified to the mission control 200 through periodic alert messages. The mission control 200 may then send highly specific notifications to vehicles within the motorbike awareness area, defined beforehand, as above-described.

According to another embodiment the mission control 200 may be further configured to send early warning notifications to road-side communication units of the mobile network infrastructure, wherein the notifications may be related to an expected arrival of a detected motorbike and may contain all relevant information about the detected motorbike (e.g., risk-level, speed, etc.). Consequently, upon detection of the motorbike by road-side units (RSU), they are immediately able to send notifications to the vehicles 350 in the motorbike awareness area 340 without any delays incurred due to the processing of sensed data by the mission control 200.

According to another embodiment detection means of other vehicles on the road may be configured to assist in the motorbike detection. For instance, sensors installed in or on cars (such as, e.g., radar, lidar, cameras, ultrasonic sensors, etc.) may be configured to detect motorbikes in their proximity and to expose to the mission control system 200 obtainable information, such as information about a VRU (Vulnerable Road Users) type of a detected motorbike, a number of motorbikes (of a detected group of motorcyclists) and/or an assessment of a severity level, speed, direction movement of the detected motorbike. It should be noted that it in case multiple different entities are involved in the motorbike detection process and report their detected information to the mission control system 200, it may happen that the mission control system 200 receives complementary or duplicate information of the same motorbike. Therefore, the mission control system 200 may be configured to apply appropriate means to process and combine the received information to resolve the desired information.

According to embodiments, data about motorcycles may be provided as gateway- based contextual information. In this context it may be provided that motorcycles are equipped with a gateway device with network connectivity that is connected to the roadside network infrastructure through existing technologies, such as LTE, or LTE-Advanced or 5G. Such a gateway may be configured to continuously send a set of information to the network infrastructure, from where it is forwarded to the mission control 200, such as location, speed, types of vehicle. An activity diagram for such a scenario is depicted in Fig. 7. The data model used in the activity diagram of Fig. 7 is the following:

In contrast to the embodiment shown in Fig. 6, where motorcycle detection is performed via BS 100, according to the embodiment of Fig. 7 the motorcycles report their contextual information proactively to the mobile network infrastructure, i.e. BS 100, via a motorbike installed smart gateway 700 that is connected to the mobile network infrastructure. In particular, as shown at step 1a., the gateway 700 transmits a LOC_UPDATE_REQ to the BS 100, which in turn sends a MOT_AWARENESS_INFO message to the mission control 200 with detailed information regarding the motorbike ID and the motorbike type (step 1 b.). This type is used by the mission control 200 to perform the motorbike awareness area calculation process, as different types of motorcycles might require different area ranges to monitor. As a variant of this embodiment the gateway 700 may be configured to infer a dangerousness level based on the past motorcyclist behaviors. However, such information should not disclose the identity of the motorbike. According to a further variant the gateway 700 installed on the motorcycle may be connected with the motorcyclist’s helmet, which enables the provision of augmented-reality-based contextual information. For instance, the helmet may be constructed as an acoustic helmet that is connected (via a short-range communication technology) to the smart gateway 700 to exchange only audio data (e.g., to implement inter-communication between motor-bikers). Alternatively, the helmet may be constructed as an augmented reality, AR, helmet that is connected (via a short-range communication technology) to the smart gateway 700 to exchange audio and video data which is then directly displayed on the helmet visor. While this gives an incentive to the motorcyclists to install such gateway in order to get useful information while driving (e.g. by offering a see-through service), it may also send information about motorcyclists’ motion speed and locations via the mobile network infrastructure to the mission control 200. Many modifications and other embodiments of the invention set forth herein will come to mind to the one skilled in the art to which the invention pertains having the benefit of the teachings presented in the foregoing description and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.