WO2018134831A1

WO2018134831A1 - System and method for enhancing road security

Info

Publication number: WO2018134831A1
Application number: PCT/IL2018/050085
Authority: WO
Inventors: Aviram MALIK
Original assignee: Individual
Current assignee: Individual
Priority date: 2017-01-23
Filing date: 2018-01-23
Publication date: 2018-07-26
Anticipated expiration: 2019-07-23

Abstract

A method for enhancing road security in which a plurality of Communicated Road Users (CRUs) are registered for receiving a service. Location information is provided by the CRUs to facilitate performing risk analysis at a server, which takes into consideration other data such as map data as well. A risk estimate provided for each registered CRU respectively. Adaptive data rate communication may be implemented.

Description

SYSTEM AND METHOD FOR ENHANCING

ROAD SECURITY

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of priority to US Provisional Patent Application Serial Number 624499130, filed 23, January 2017, entitled "Auxiliary Means for Enhancing Systems for Preventing Accidents". The aforementioned application is hereby incorporated herein by reference.

Technical Field

The present invention relates to improving efficiency of Intelligent Transportation Systems (ITSs) and, in particular, to improvement in usage of computerized resources of ITSs.

Background

It is anticipated that in the near future our roads will serve users with various degrees of autonomy. This may require a growing sophistication of accident prevention systems which are capable of swiftly handling a large amount of data input. ITS is an advanced application which aims to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. Smart phones having various sensors can be used to track traffic speed and density. The data from smartphone accelerometers used by vehicle drivers is monitored to find out traffic speed and road quality. Other data generated by smartphones, such as GPS tagging, or more generally, geotagging of smart phones enable identification of traffic density and possible traffic jams.

Some ITSs are proposed for example in US 6405132, US 9008890, and CA 2299969. Deficiency in system resources might cause deterioration of the efficiency of such ITSs, even to the point of risk to users of these systems. System resources such as processors, memory, or bandwidth on a communication bus are often shared by various components. The time-varying nature of the input traffic makes it difficult to analyze behaviors such as congestion, overload, end-to-end latencies, or packet loss. The challenge is to efficiently model the system's response to changing input traffic patterns. These systems are best modeled as event-driven or activity-based systems, since the input traffic activity places the load on the shared resource. The resource constraints associated with embedded systems, introduce non-determinism in the realtime system. Risk analysis is the process of analyzing and defining the dangers to individuals, groups or organizations. An example of a traffic risk analysis methodology may be found in a Traffic Management Assessment Form of the University of Melbourne. According to this suggested methodology the risk rating (shown as step 2), includes risk matrix of two variables and a calculation with three variables. The two variables being used in the matrix are: likelihood, and consequence. Generalized linear models (GLM) are widely used in traffic accidents analysis. Two types of GLMs are which are used are Poisson regression models and negative binomial regression models. Usually, the Poisson distribution is suitable to describe count data such as traffic accidents, because accidents occur only rarely. When traffic accidents are aggregated by geographical meshes, however, there exist excessive meshes which have a zero accident count. This leads to over dispersion. The Poisson distribution that has the same variance value as its mean cannot handle over dispersed data well.

Driving is a complex activity that requires multi-level skilled behavior. Yet much of our driving behavior is automatic, developed from experiences and requiring minimal allocations of attention. This automatic aspect of behavior is based on mental and psycho-physical programs called schema. We develop schema for our driving over time but in an ad hoc manner based on our perception of driving situations. Schema helps us deal with every day driving occurrences such as negotiating traffic, intersections and road conditions.

Most fatigue-related accidents occur during normal sleeping hours, and the more severe the crash, the more likely it is that the driver or drivers were fatigued. Fatigue is a likely factor in almost one third of single-vehicle crashes in rural areas. Drowsy driving is the dangerous combination of driving and sleepiness or fatigue. This usually happens when a driver has not slept enough, but it can also happen due to untreated sleep disorders, medications, alcohol drinking, or shift work. Being fatigued makes drivers less aware of what is happening on the road and impairs their ability to respond quickly and safely if a dangerous situation arises.

In emergency situations our schema or automatic behavior may not be appropriate and shifting our behavior more consciously to control such rare events is unlikely to succeed because we do not have effective emergency behavior or schema, at our disposal. Behavior will reflect the psychological profile of each individual and the situations the individual is exposed to. Behavior may be aggressive, passive, distracted, alert, ignorant, confident, timid, skilled, arrogant, tolerant, angry, polite, competent, and vindictive or many other descriptions. It is commonly understood that individual differences play a role in safe driving performance. Therefore, regardless of efforts by organizations to manage work-related road safety, some individuals are more likely to exhibit safe driving behavior than others. Studies suggest that there exists a correlation between personal profile and unsafe driving: Men were more likely to have an accident than women were; The nature of the accidents experienced by men and women were different; Younger drivers were at greater risk than were older drivers; There was generally a greater risk associated with increased levels of education; Certain personality characteristics were associated with increased risk; the most reported characteristics being 'Sensation Seeking' and risk-taking; There was a relationship between social deviance and violations and accidents; There was a decreased risk of accident involvement with experience, although this tended to even out after eight years of experience; Stress was associated with increased accidents as were major life events; There was increased risk associated with certain medical conditions; There were differences across different ethnic groups. Studies indicate a clear relation between high aggression scores and accidents. Violence and aggressiveness in traffic are frequently encountered social phenomena, with important social and economic consequences. The literature in the field has focused on traffic congestion, driver stress, and the role of gender in violence and aggressiveness, being marked upward trend and professionals' attempts to provide solutions for the reduction of such phenomena. The occurrence of both anger and aggression in traffic depends on personal and situational aspects.

Objectively determining what aggressive behavior is proves to be quite difficult, because much depends on individual interpretation. Emotions consist of physical and mental sensations expressed in conjunction with thoughts. Emotions can cause thoughts or can themselves be caused by thoughts. Furthermore, emotions are accompanied by an inclination to undertake action or to refrain from doing so. This can result in avoidance behavior or in approach behavior. Avoidance behavior mainly occurs with negative emotions (such as anger) and approach behavior mainly with positive emotions (such as happiness). There is no general definition of driver aggression however different descriptions can be found. In many of these behavioral definitions the following characteristics of aggression in traffic are given: behavior that may cause physical or emotional harm to a fellow road user; behavior expressions that consists of a violation of moral standards. Various studies indicate that anger and aggressive behavior in traffic decreases as one grows older, that men show (physically) aggressive behavior more frequently than women, and that people who tend to get angry, are also quicker to show aggressive behavior in traffic. Concerning the traffic situation, driver aggression is often said to be stronger in situations in which the personal own interests are prejudiced and frustration arises: the so-called frustration-aggression theory (Dollard et al., 1939). Typical situations include traffic jams, red light delays, or violations committed by others that cause inconvenience. Research has shown that anger can lead to a higher speed and that an annoying event can lead to immediate acceleration.

Summary

According to some aspect of the present invention there is provided a method for enhancing road security comprising the steps of: registering a plurality of Communicated Road User (CRU) for a service provided; receiving locations from respective registered CRUs; performing risk analysis based on at least said received locations, at a server producing risk estimate for each such CRU and sending said own estimate output to at least one respective said CRUs CRUs, and wherein, said analysis relates to hazardous road situations.

According to some embodiments of the invention the method includes least one portable communications device, allocated to each CRU of said plurality of CRU's.

According to some embodiments of the invention the method includes risk analysis involving map data.

According to some embodiments of the invention the method includes risk analysis which includes calculations of probability and/or possibility of collisions.

According to some embodiments of the invention the method includes data output from a server/processor which includes warning and/or commands according to said probability. According to some embodiments of the invention the method includes CRUs which are selected from the group consisting of autonomous vehicle, driven vehicle, pedestrian, and robot.

According to some embodiments of the invention the method includes CRUs which public transportation means.

According to some embodiments of the invention the method includes CRUs transported in a mean of public transportation and which are unified during said transportation.

According to some embodiments of the invention the method includes receiving data selected from the group consisting of visual, voice, traffic, emergency events, and weather.

According to some embodiments of the invention the method includes receiving data originated from sources selected from the group consisting of social networks, authorities, GPS, and any combination thereof.

According to some embodiments of the invention the method includes learning and characterizing behavior of CRUs.

According to some embodiments of the invention the method includes performing behavioral analysis based on a data input concerning behavior patterns of said CRUs.

According to some embodiments of the invention the method includes characterizing behavior comprises grouping of said CRUs according to attributes selected from the group consisting of vehicle, autonomy, age, gender, artificiality, biological species, responsiveness, aggressiveness, obedience, and personal history. According to some embodiments of the invention the method includes comparing said data input of behavior patterns of said CRUs with average behavior patterns of said CRUs, or with data input of predefined behavior patterns.

According to some embodiments of the invention the method includes risk analysis which includes ranking road locations according to their potential risk.

According to some embodiments of the invention the method includes learning and characterizing road locations.

According to some embodiments of the invention the method includes processing of data retrieved from at least one map.

According to some embodiments of the invention the method includes receiving and updating input data regarding road infrastructure, and performing deep learning for updating said maps.

According to some embodiments of the invention the method includes wherein said portable communication devices are cellular phones.

According to some embodiments of the invention the method includes communicating and sharing data among plurality of said CRUs.

According to some embodiments of the invention the method includes locations that are sent periodically from said portable communications devices.

According to some embodiments of the invention the method further includes receiving data of parameters selected from the group consisting of speeds and accelerations, decelerations, and steering.

According to some embodiments of the invention the method includes changing a CRU properties according said data of parameters, and/or data input entered by a user. According to some embodiments of the invention the method includes locations that are indicated with a geometric shape.

According to some embodiments of the invention the method further includes receiving data input from said plurality of portable communication devices comprises data of GPS and/or odometry and/or accelerometer and/or gyroscope.

According to some embodiments of the invention the method includes performing risk analysis comprises predicting possible collisions.

According to some embodiments of the invention the method further comprising receiving data input from a plurality of sensors.

According to some embodiments of the invention the method includes sensors that are installed in locations selected from the group consisting of road infrastructure, building, air vehicles, and road vehicles.

According to some embodiments of the invention the method includes connecting to a communication network.

According to some embodiments of the invention the method includes correlating update rate of data transfer among said processor, said communication module, and said plurality of portable communication devises, according to risks ranking, wherein said risks ranking is based on said analysis.

According to some embodiments of the invention the method includes receiving data input from plurality of sources communicating with said processor, wherein the number of said sources is determined according to a defined degree of emergency retrieved from at least one data base module and/or received from external source and/or generated by an algorithm. According to some embodiments of the invention the method includes receiving data input from plurality of sources communicating with said processor, wherein the update rate of input from said sources is determined according to a degree of emergency retrieved from at least one data base module and/or received from external source, and/or generated by an algorithm.

According to some embodiments of the invention the method includes using external source of data input such as human, or a communication network.

According to some embodiments of the invention the method further includes receiving GPS data by said cellular phones; processing said GPS data by a cellular phone application; receiving said processed GPS data by an open socket of a server; and processing said data received by said open socket.

According to some embodiments of the invention the method includes publishing GPS data and generating receipt acknowledgements for http requests received by the processor, said receipt acknowledgements are sent to cellular phones of said road CRUs.

According to some embodiments of the invention the method includes risk analysis which is done by an algorithm which begins whenever said processor receives new GPS position from a Web socket, wherein said performing risk analysis is carried out for each said portable communication device, and wherein said sending output data concerning said risk analysis is sent from said processor to a plurality of Web sockets and to at least one database module. According to some embodiments of the invention the method includes risk analysis that includes computations of values of braking strength, remaining distance to a junction, speed, acceleration/deceleration, time left to junction, distance until full braking, direction relative to junction, warning window (WW), and geographical directions.

According to some embodiments of the invention the method further includes activating CRU masking of at least one of said road CRUs according to thresholds predefined for said values; ending said algorithm when said masking is active; and verifying probable collision direction of at least one road CRU with at least one object.

According to some embodiments of the invention the method includes grouping of said CRUs having similar times to probable collision or to hazardous road location, such that members of each group have similar remaining times to said probable collision or to said hazardous road location.

According to some aspects of the invention there is provided a system for preventing accidents which includes: at least one processor capable of performing risk analysis and calculations of probability and possibility of road collisions; at least one communication module communicably coupled with said processor; a plurality of portable communications devices communicably coupled with said communication module; and wherein said at least one processor is capable of tracking locations received from said plurality of portable communications devices, and sends output to said plurality of portable communications devices.

According to some embodiments of the invention the system includes a portable communications devices, appended to Communicated Road Users (CRUs)

According to some embodiments of the invention the system receives traffic updates and warnings of probable accidents.

According to some embodiments of the invention the system includes a communication network.

According to some embodiments of the invention the system includes communication module which is an MQTT socket and said processor which is communicably coupled with database module, and wherein said performing risk analysis calculations is done by an algorithm.

Brief description of Drawings

Preferred embodiments, features, aspects and advantages of the present invention are described herein in conjunction with the following drawings:

Figure 1. schematically illustrates a general view of system architecture according to some embodiments of the present invention

Figure 2. shows a flowchart, schematically illustrating user's cellular phone application algorithm according to some embodiments of the invention

Figure 3. shows a table of mobile/server interface definitions according to some embodiments of the invention

Figure 4. shows an exemplary table of parameters to be entered via a setting screen, and their default values .

Figure 5. depicts a mobile application's main page design according to some embodiments of the present invention

Figure 6. depicts a mobile application's map page design according to some embodiments of the present invention

Figure 7. schematically illustrates a general view of data transfer relating to risk assessments made by components included in a system according to the invention and its related updates

Figure 8. shows a flowchart, schematically illustrating a server mode of operation according to some embodiments of the invention

Figure 9. shows a flowchart, schematically illustrating run algorithm according to some embodiments of the invention Figure 10. shows a graph, depicting deceleration caused by different forces of brake in an initial speed of lOOKMH

Figure 11. shows a graph with curves depicting "valid warning windows" that correspond to different decelerations

Description of Embodiments

The inventor of the present invention had realized that a way to enhance ITSs performance is by providing a system that is capable of receiving data input from a plurality of communicated road users (CRUs) in order to perform computerized risk analysis which include probability/possibility of collisions. Such is handled by at least one processor which accepts data from the CRUs and optionally other sources and sends data back to the CRUs through their respective portable communications devices. The data generated and sent by a server may include warnings and/or commands, for example to autonomous vehicles or to other CRUs. A distinction is made herein between a road user in general, and a Communicated Road User (CRU). Typical CRUs are pedestrians carrying cell phones, or autonomous vehicles carrying an appropriate communications system. Thus it may be understood that every CRU is a road user but not every road user is a CRU. In general, a CRU carries on board a communications device which is a means by which it communicates with other elements of the system such as the server. An individual CRU may change properties by for example relocating the communications device, at times carried by a pedestrian at other time the same communications device is carried by a bicycle rider, and yet at another time the same communications device is carried on board a vehicle. A system according to some embodiments of the invention may detect, (recognize) changes in properties of CRUs as intentionally effected by the user. Unintentionally, the properties are changed based on measured parameters such as speed of travel which may characterize each CRU. Regarding mass or public transportation, a public transportation means (e.g., train, ship, aircraft, autonomous vehicle) may include a number of otherwise regarded separate CRUs, however when those CRU's are transported in such means of public transportation, they may be considered as a unified CRU during their use of the public transportation. In such a case, when excluding, otherwise regarded separate CRUs, each mean of public transportation, whether being autonomous or driven, may constitute a single CRU.

According to some embodiments of the invention at least one portable communications device is allocated to each CRU of the registered plurality of CRU's.

A system according to some embodiments of the invention includes intelligent control of the amount of data being communicated among components of a system embodying aspects of the present invention. However, to accomplish the service provided by the present invention, first a registry of a plurality CRUs is to be compiled in order to create a CRU database and receive data from the available CRUs. It is preferable to register as many CRUs as possible to the service provided. This is because a road user which is not registered provides less data, if at all, to the risk analysis.

Moreover, a system according to some embodiments of the invention may include a dynamic artificial intelligent component which "learns" and characterizes behavior of road users as well as road locations, in order to predict hazardous events. Accordingly, some aspects of the present invention may include means for performing behavioral analysis based on data input that relates to behavioral patterns of road users. In this disclosure, aspects of the present invention are explained by way of example. However, it should be understood that the below examples are in no way limiting embodiments of the present invention. According to some embodiments, there is provided a system that includes at least one processor or a server for performing computations and risk analysis based upon input data received via communications means from a plurality of portable communications devices and in some cases also from a plurality of sensors. The terms "computation" and "calculation" may be used interchangeably hereinafter. The terms "sensor" and "detector" may be used interchangeably hereinafter. The system may include one or more memory means. According to some embodiments of the invention, risk analysis includes processing of data retrieved from maps. Maps may be obtained from various sources including by using "deep learning". According to some embodiments of the invention the aforementioned portable communication devices are allocated to autonomous vehicles.

According to some embodiments of the present invention the above mentioned input data includes information regarding locations and travelling parameters (speed, acceleration, direction etc.,) of the aforementioned portable communications devices.

A system embodying the present invention may include a server that tracks mobile devices locations and their movements, inter alia, in concordance with locations messages, which are sent periodically from mobile devices, to the server. Accordingly the server calculates, estimates, and predicts possible collisions. The server sends updates/warnings to the mobile devices regarding relevant risks of collision.

According to some embodiments of the present invention, data received by the processor which is typically generated periodically by the portable communication devices and/or sensors.

The processor may perform calculations for prediction of possible collisions between CRUs (e.g., autonomous vehicles), based on the data described hereinabove, and may send output data to be received by portable communications devices concerning risks of collisions.

According to some embodiments of the present invention, some components of the system are installed on a vehicle (e.g., portable communication devices and/or sensors), and can be connected to various sensors installed in the vehicle some of which generate input regarding the vehicle surroundings. Additionally, some of the system's components (e.g., portable communication devices), according to some embodiments of the invention may communicate with other CRUs as well as with a variety of communication means, such as sensors, satellites (possibly via "internet of things"). Some input for the system may be generated by human user.

The system according to some embodiments of the invention includes a communications network for integrating system components. The system may utilize external communication networks as well.

Adaptive input rate from CRUs

In computer science, resource contention is a term which is associated with conflict over access to a shared resource such as random access memory, disk storage, cache memory, internal buses or external network devices. Contention problems may result in a number of problems, including deadlock, livelock, and thrashing.

A way for enhancing system performance and improving the usage of computerized resources in accordance with some embodiments of the present invention is achieved by correlating update rate to risk ranking. Thus, optionally an algorithm is utilized for determining the input rate from sources to use in relation to degree of emergency the relevant CRU is awarded. This adaptive rate control situations in which the activity of many input sources downgrades system performance putting some CRUs in a compromised situation The degree of emergency may be retrieved from at least one data base module and/or received from external source, and/or generated by an algorithm.

According to an embodiment of the present invention the system generates predictions for collisions, based on calculations of risks. Those calculations may be based on parameters related with CRUs (e.g., autonomous vehicles), such as their locations, speeds, and their respective acceleration. The degree of uncertainty associated with the accuracy of the system in determining the location of objects can be expressed by some geometrical forms such as an ellipse with dimensions corresponding to such inaccuracies. However other shapes may be used instead or in addition to ellipses, e.g., circles or polygons. The size of the shapes may be correlated with parameters such as speed, acceleration, and risk assessment. Calculations of risks may include using of GPS data and/or odometry and/or accelerometer and/or gyroscope data from smartphones. Gyroscope input may indicate portable communications devices positions. Sometimes such input regarding communication device position may indicate loss of control by a CRU (e.g., a person driving a vehicle). This may affect the risk calculation (i.e., increase the risk associated with communication device operated out of control). Risk analysis may be supported inter alia by extrapolation of vectors, probability calculations and statistics. Locations in roads may be ranked according to their risk. For example, junctions as well as curvatures are locations typically with comparatively high degree of risk, especially when a CRU performs a dangerous approach. According to some embodiments of the present invention junctions may be virtual, i.e., the processor "builds" a hypothetical junction whenever at least one CRU is collision track with at least one other object. Those danger situations may be characterized and predicted, by making use of input regarding acceleration, deceleration, braking, steering and speeds of travelling CRUs. In accordance with some embodiments of the present invention, a determination of danger situation is done by comparison of real time data input received by the processor with standard traffic rules. Computations and risk analysis that may be made by the processor may be based on many sorts of data input such as generated by visual imaging systems, of voice signals, and of information regarding weather. Input being received from multiple sources such as those described before, may assist in generating a more comprehensive picture of a dangerous situation based on "big data". Additional data used by the processor may include social networks, GPS, information from local authorities, traffic signs, traffic lights, pedestrians' crossings, intersections with railroads, and other infrastructure elements affecting traffic. Real time information regarding traffic may be taken into account for assessing risks. For example road loads, day hours, and lighting. These can be used in some cases for adding extra safety margins. Information about traffic events may be used also as input for risks analysis. Such events may include road works, school hours, demonstrations, and emergency situations.

It is possible that the risk assessment will include data input regarding typical behavioral profiles of groups of road CRUs or of specific road CRUs. CRUs may be characterized for example, according to the vehicle that they use, or according to their level of autonomy, personality, and age. Some road users (including CRUs), may not be humans such as animals, autonomous vehicles, or autonomous machines. Characterizing CRUs' behavior may refer to patterns which can be related, for example, to aggression, fatigue, distraction or obedience. Assessment of risks associated with CRUs may include average values, and deviations. Another feature according to some embodiments of the present invention is adjusting the risk analysis to each particular CRU or CRU type. Road users may differ in many aspects such as in their degree of autonomy, personal profile, temporal psychic status, vehicle, age, being a human, autonomous vehicle, robot or an animal.

Example 1- System architecture according to some embodiments of the invention

Figure 1 shows a general scheme of system architecture according to some embodiments of the present invention. Accordingly, GPS input (10) is received in the CRUs' cellular phones, and is processed by cellular phones applications (20). Http requests, being generated by the system's cellular applications (20), are received by an MQTT socket (40) of a server (50) having database (60) and an algorithm (70) for processing the above input.

The CRU's cellular phone application algorithm according to some embodiments of the invention is shown schematically in figure 2. Upon receipt of data by an open socket (110) two routes which become optional. First route (120) includes reading received GPS data (122). After reading is completed, GPS data is published (124). In a second route (130) after a receipt of MQTT message (132), the cellular system's application screens are being updated (134), e.g., "main page" and "map page". CRU updates (141) via a setting screen (142) are initiating http requests to the server (144) which in turn sends back receipt acknowledgements (146); both are included in the bidirectional communication between the CRUs' cellular phones and the server (140), as shown in more general manner in figure 1. Mobile/server interface definitions according to some embodiments of the invention are shown in a table in figure 3. An example for parameters input via a setting screen, and their default values according to some embodiments of the present invention appears in a table shown in figure 4. Mobile application's pages designs according to some embodiments of the present invention are shown schematically in figure 5 (main page), and in figure 6 (map page).

The data transfer adaptation to risk assessments made in components included in a system according to the invention and its related updates, received by CRUs' cellular applications are depicted in figure 7. Accordingly, GPS input (10) being received in CRUs' cellular phones, and is processed by cellular phones applications (20). Data regarding locations which is generated by cellular phones applications (12) is received in at least one communication socket (45) and passed to at least one algorithm module (75) and to at least one data base module (61). The terms "communication socket" and "communication module" may be used interchangeably hereinafter. Risk assessments are performed by the algorithm module (75), being based on input regarding locations (12) and other data input, discussed hereinabove and below. Subsequently, data input generated by at least one algorithm module regarding the risk assessments (147), is received by at least one communication socket (45). The cellular applications (20) receive the risk assessments data published by the communication socket (45) which can be used to generate alarms and/or actions intended to prevent collisions by road users.

A server mode of operation according to some embodiments of the invention, (200) is shown schematically in figure 8. The junction is defined as a Web socket (205). Each mobile device transmits its location, (e.g., provided by geotagging) irrespective to other mobile devices. The algorithm begins a new calculation when it receives new GPS positions from the Web Socket (210). For each run of the algorithm, (220), a calculation of the risk assessment is carried out for each device. The results are sent (230) to the WEB Sockets (publish), and also are stored in a data base (240). Data may be saved in the following format: <session ID> <user name> <mobile ID> <GPS info <distance to a junction> <Risk Estimation>

Run algorithm "step 1" according to some embodiments of the invention is shown schematically in figure 9 (300), wherein the following values are calculated for each road CRU (310): braking strength (bi), distance to target junction (d), speeds (v , acceleration/deceleration (ai), time left to target junction (Ti), full braking distances (di), direction to junction (in/out), and geographical directions (N, S, W, E). CRU masking (320) becomes active according to values of the above parameters for a specific road CRU. If CRU masking (320) is active, the algorithm ends for the masked CRU. For CRUs whose masking (320) is inactive, the server conducts verification for each CRU whether it is in a probable collision direction with at least one object (330). If result of verification (330) is "no", the algorithm ends. If the result is "yes" the server performs "step 2", risks calculation and assessments (340).

Example 2- "Warning Windows" determination according to some embodiments of the present invention

A system according to some embodiment of the invention may provide a "valid warning window" (WW) in case a brake was not made at the right time in sufficient force, i.e., at least around -1/2G (about -5m/sec²), which is defined here as an aggressive brake. The terms "deceleration" and "brake force" may be used herein interchangeably. A "warning window" as used herein, is a distance before a "crash" (collision of a CRU with another object), in which the CRU may be warned, thereby allowing the CRU to take an action to avoid the crash, or in other words, time, before a probable collision, that is required to warn a CRU in order to prevent collision. The terms "valid warning window" and "warning window" may be used herein interchangeably.

Such WW may be implemented even if there is no anticipation for collision, rather before hazardous locations.

The graphical illustration shown in figure depicts deceleration caused by different braking forces at initial speed of 100 KMH. X-axis represents distance to collision or to a hazardous location; and Y axis represent speed KMH (kilometer per hour). As may be observed in the graph, each curve correspond to a different braking force and the braking starts at different distance for each case where a different braking forces are implemented however resulting a same point of end of deceleration. The curves which are denoted "too late" relate to the braking force of -8.5m/sec² when is implemented at a distance too close to probable collision point or hazardous location (positive speed at collision point). A normal brake force is defined as approximately - 1/4G. According to some embodiments of the invention, WWs, typically in terms of time or distance are set, based on such graphs, or their respective equations. According to the graph in figure 10, thevalues obtained for WWs depending on braking force which is implemented above 5m/sec² ranges between 40 to 80 meters before junction (at initial speed of 100 KMH). It should be emphasized that these values presented herein are for example of a particular case and do not limit the present invention.

According to some embodiments of the present invention WWs may be determined based on the prediction of "time to accident". The graph in figure 11 is related to determination of WWs where the X axis represents the prediction of "time to accident" in seconds and the Y axis represents speeds in KMH. The curves in the graph correspond to different decelerations. According to some embodiments of the invention, provided that CRU masking disclosed herein is inactive, run algorithm "step 2" disclosed herein, may be executed with grouping of road CRUs having similar times to probable collision (or to hazardous road location). Each group consisting of similar remaining times to probable collision may be herein referred as "a time interval" or "ΔΤί".

Masking

According to some embodiments of the invention, various CRU masking thresholds and combinations thereof may be implemented. For example, if deceleration is detected, at the last 200 milliseconds before a probable collision, or if there has been detected a significant brake indication (e.g., >2.5m/sec²) during the last 200 milliseconds before probable collision. Those masking options may help to restrict the "mobile to server" latency.

Those masking options may help to avoid lengthening of server latency.

Example 3~ Main components of a system according to some embodiments of the invention

Main components according to some embodiments of the invention are: mobile application run on Android OS 4.0 or higher and a server application that includes two parts: communications module based on web technology SOCKET (Logos IP) and calculations module - for executing protocols. The mobile application supports at least three screens: main screen, map screen and setup screen. The main screen may display: status indicator, directions to hazardous locations (e.g., junctions), distance to hazardous locations, speed, acceleration, and of risk ranking which may be represented in a 0-100% scale. Setup screens (possibly touch screens), may include display fields and input fields for parameters and thresholds such as: maximal speed; the vehicle features (e.g., dimensions, weight, and braking force); and for the cellular phone location in the vehicle.

Claims

1. A method for enhancing road security comprising the steps of:

• registering a plurality of Communicated Road User (CRU) for a service provided;

• receiving locations from respective registered CRUs;

• performing risk analysis based on at least said received locations, at a server

• producing risk estimate for each such CRU and

• sending said own estimate output to at least one respective said CRUs CRUs, and wherein, said analysis relates to hazardous road situations.

2. The method of claim 1 wherein at least one portable communications device is allocated to each CRU of said plurality of CRU's.

3. The method of claim 1 wherein said risk analysis includes map data.

4. The method of claim 1, wherein said risk analysis comprises calculations of probability and/or possibility of collisions.

5. The method of claim 5, wherein said data output comprise warning and/or commands according to said probability.

6. The method of claim 2, wherein said CRUs are selected from the group consisting of autonomous vehicle, driven vehicle, pedestrian, and robot.

7. The method of claim 2, wherein said CRUs comprise public transportation.

8. The method of claim 7, wherein CRUs transported in a mean of public transportation are unified during said transportation.

9. The method of claim 1, further comprising receiving data selected from the group consisting of visual, voice, traffic, emergency events, and weather.

10. The method of claim 9, wherein said receiving data comprise data originated from sources selected from the group consisting of social networks, authorities, GPS, and any combination thereof.

11. The method of claim 9, comprising learning and characterizing behavior of CRUs.

12. The method of claim 11, comprising performing behavioral analysis based on a data input concerning behavior patterns of said CRUs.

13. The method of claim 11, wherein said characterizing behavior comprises grouping of said CRUs according to attributes selected from the group consisting of vehicle, autonomy, age, gender, artificiality, biological species, responsiveness, aggressiveness, obedience, and personal history.

14. The method of claim 12, comprising comparing said data input of behavior patterns of said CRUs with average behavior patterns of said CRUs, or with data input of predefined behavior patterns.

15. The method of claim 1, wherein said risk analysis comprises ranking road locations according to their potential risk.

16. The method of claim 9, comprising learning and characterizing road locations.

17. The method of claim 1, further comprising processing of data retrieved from at least one map.

18. The method of claim 17, further comprising receiving and updating input data regarding road infrastructure, and performing deep learning for updating said maps.

19. The method of claim 1, wherein said portable communication devices are cellular phones.

20. The method of claim 1, further comprising communicating and sharing data among plurality of said CRUs.

21. The method of claim 1, wherein said locations are sent periodically from said portable communications devices.

22. The method of claim 1, further comprises receiving data of parameters selected from the group consisting of speeds and accelerations, decelerations, and steering.

23. The method of claim 22, comprising changing a CRU properties according said data of parameters, and/or data input entered by a user.

24. The method of claim 1, wherein said locations are indicated with a geometric shape.

25. The method of claim 1, further comprises receiving data input from said plurality of portable communication devices comprises data of GPS and/or odometry and/or accelerometer and/or gyroscope.

26. The method of claim 1, wherein said performing risk analysis comprises predicting possible collisions.

27. The method of claim 1, further comprising receiving data input from a plurality of sensors.

28. The method of claim 27, wherein said sensors are installed in locations selected from the group consisting of road infrastructure, building, air vehicles, and road vehicles.

29. The method of claim 1, further comprising connecting to a communication network.

30. The method of claim 1, further comprising correlating update rate of data transfer among said processor, said communication module, and said plurality of portable communication devises, according to risks ranking, wherein said risks ranking is based on said analysis.

31. The method of claim 1, further comprising receiving data input from plurality of sources communicating with said processor, wherein the number of said sources is determined according to a defined degree of emergency retrieved from at least one data base module and/or received from external source and/or generated by an algorithm.

32. The method of claim 1, further comprising receiving data input from plurality of sources communicating with said processor, wherein the update rate of input from said sources is determined according to a degree of emergency retrieved from at least one data base module and/or received from external source, and/or generated by an algorithm.

33. The method of claim 32, wherein said external source is human, or a communication network.

34. The method of claim 25, further comprising receiving GPS data by said cellular phones; processing said GPS data by a cellular phone application; receiving said processed GPS data by an open socket of a server; and processing said data received by said open socket.

35. The method of claim 34, comprising publishing GPS data and generating receipt acknowledgements for http requests received by the processor, said receipt acknowledgements are sent to cellular phones of said road CRUs.

36. The method of claim 1, wherein said risk analysis is done by an algorithm which begins whenever said processor receives new GPS position from a Web socket, wherein said performing risk analysis is carried out for each said portable communication device, and wherein said sending output data concerning said risk analysis is sent from said processor to a plurality of Web sockets and to at least one database module.

37. The method of claim 36, wherein said risk analysis comprises computations of values of braking strength, remaining distance to a junction, speed, acceleration/deceleration, time left to junction, distance until full braking, direction relative to junction, warning window (WW), and geographical directions.

38. The method of claim 37 further comprising: activating CRU masking of at least one of said road CRUs according to thresholds predefined for said values; ending said algorithm when said masking is active; and verifying probable collision direction of at least one road CRU with at least one object.

39. The method of claim 38, comprising grouping of said CRUs having similar times to probable collision or to hazardous road location, such that members of each group have similar remaining times to said probable collision or to said hazardous road location.

40. A system for preventing accidents comprising: at least one processor capable of performing risk analysis and calculations of probability and possibility of road collisions; at least one communication module communicably coupled with said processor; a plurality of portable communications devices communicably coupled with said communication module; and wherein said at least one processor is capable of tracking locations received from said plurality of portable communications devices, and sends output to said plurality of portable communications devices.

41. The system of claim 40, wherein said portable communications devices are appended to Communicated Road Users (CRUs)

42. The system of claim 40, wherein said output comprises traffic updates and warnings of probable accidents.

43. The system of claim 40, further comprising a communication network.

44. The system of claim 41, wherein said communication module is an MQTT socket and said processor is communicably coupled with database module, and wherein said performing risk analysis calculations is done by an algorithm.