WO2022219650A1

WO2022219650A1 - System and method for automated collision avoidance

Info

Publication number: WO2022219650A1
Application number: PCT/IN2022/050360
Authority: WO
Inventors: Kalash NIBJIYA; Rishi Kant RAJPOOT
Original assignee: Bidaal Technology Private Limited
Priority date: 2021-04-14
Filing date: 2022-04-14
Publication date: 2022-10-20

Abstract

Disclosed is a collision avoidance system (100) comprising a detection unit (102) configured to sense signals representing (i) a direction of motion of a plurality of objects (200a-200n) and (ii) a speed of the plurality of objects (200a-200n). The detection unit (102) is configured to classify each object of the plurality of objects (200a-200n) as one of, a human, a machine, and a category of the machine based on the sensed signals. Processing circuitry (108) configured to receive the sensed signals from the detection unit (102) determine a probability of collision for each object of the plurality of objects (200a-200n) based on the sensed signals and the classification, the probability is determined for (i) a human-machine collision and (ii) a machine-machine collision, actuate a traffic control peripheral (106) to pass an object of the plurality of objects (200a-200n) through the junction (202) based on the determined probability.

Description

SYSTEM AND METHOD FOR AUTOMATED COLLISION AVOIDANCE

TECHNICAL FIELD

The present disclosure relates to a collision mitigation system, and more particularly to a system and a method for automated collision avoidance. BACKGROUND

With growing number of vehicles on roadways, need is to take initiative to decrease the number of collisions occurring on the roadways. The chance of collision increases when more than one vehicle is heading towards a junction at the same time, that may lead to man-machine and machine-machine collision. Specifically, in heavy vehicles, it is extremely difficult to see adjacent machines and other heavy vehicles that may causes collision.

Many advancements have led to improve safety systems for vehicles in sensor technology. The arrangements and methods for detecting and avoiding collision are available. These systems have limitations as they have less accuracy to mitigate and avoid collisions.

Thus, there is a need for a technical solution that overcomes the aforementioned problems of conventional collision avoidance systems. SUMMARY

One aspect of the present disclosure provides a collision avoidance system. The collision avoidance system includes a detection unit installed on pathways leading to the junction to monitor a plurality of objects approaching the junction. The detection unit is configured to sense signals representing (i) a direction of motion of each object of the plurality of objects and (ii) a speed of each object of the plurality of objects. Further, the detection unit is configured to classify each object of the plurality of objects as one of, a human and a machine based on the sensed signals. The collision avoidance system further includes processing circuitry that is coupled to the detection unit. The processing circuitry is configured to receive the sensed signals from the detection unit. The processing circuitry is further configured to determine a probability of collision for each object of the plurality of objects based on the sensed signals and the classification. The probability is determined for (i) a human-machine collision and (ii) a machine-machine collision. Furthermore, the processing circuitry is configured to actuate a traffic control peripheral to pass an object of the plurality of objects through the junction based on the determined probability.

In some aspects of the present disclosure, the detection unit further includes a plurality of sensors. The detection unit is configured to sense signals representing (i) the direction of motion of each object of the plurality of objects and (ii) the speed of each object of the plurality of objects. In second aspect of the present disclosure, the detection unit further includes control circuitry that is coupled to the plurality of sensors. The control circuitry is configured to receive the sensed signals from each sensor of the plurality of sensors. The control circuitry is further configured to classify each object of the plurality of objects as one of, the human, the machine, and category of the machine based on the sensed signals. The control circuitry is furthermore configured to transmit detection data and the sensed signals to the processing circuitry when an object of the plurality of objects is within a predefined distance from the junction. The detection data includes a distance of the object from the junction and the classification of the object. In some aspects of the present disclosure, the processing circuitry is further configured to assign a priority to each object of the plurality of objects. The processing circuitry actuates the traffic control peripheral to pass an object of the plurality of objects through the junction based on the priority assigned to the object to avoid collision.

In some aspects of the present disclosure, the processing circuitry is further configured to generate historic data associated with each event of collision avoidance. The historic data includes at least one of, a date of an event of collision avoidance, a time of the event of collision avoidance, each object of the plurality of objects involved, and the junction involved.

In some aspects of the present disclosure, the collision avoidance system further includes a database. The database is configured to store the historic data associated with each event of collision avoidance for feedback, monitoring and training purposes. In some aspects of the present disclosure, the collision avoidance system further includes a real time clock that is coupled to the processing circuitry. The real time clock is configured to provide the date of the event of collision avoidance and the time of the event of collision avoidance to the processing circuitry. Another aspect of the present disclosure provides a method for collision avoidance at a junction. The method includes receiving, by control circuitry of a detection unit, sensed signals representing (i) a direction of motion of each object of the plurality of objects and (ii) a speed of each object of the plurality of objects from each sensor of a plurality of sensors. The method further includes classifying, by the control circuitry, each object of the plurality of objects as one of, the human, the machine, and category of the machine based on the sensed signals. Further, the method includes transmitting, by the control circuitry, the detection data to processing circuitry, when an object of the plurality of objects is within a predefined distance from the junction. The detection data includes a distance of the object from the junction and the classification of the object. Further, the method includes determining, by the processing circuitry. The probability of collision for each object of the plurality of objects based on the sensed signals and the classification. The probability is determined for (i) a man-machine collision and (ii) a machine-machine collision. Further, the method includes actuating, by the processing circuitry, a traffic control peripheral to pass an object of the plurality of objects through the junction based on the determined probability. In some aspects of the present disclosure, the method further includes assigning, by the processing circuitry, a priority to each object of the plurality of objects. The processing circuitry actuates the traffic control peripheral to pass an object of the plurality of objects through the junction based on the priority assigned to the object to avoid collision.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects, features and advantages of the embodiment will be apparent from the following detailed description when read with reference to the accompanying drawings. In the drawings, wherein like reference numerals denote corresponding parts throughout the several views:

FIG. 1 illustrates a block diagram of a collision avoidance system, in accordance with an aspect of the present disclosure;

FIG. 2 illustrates a schematic view of a setup of the collision avoidance system, in accordance with an aspect of the present disclosure; FIG. 3 illustrates a flowchart of a method for avoidance of collision of a plurality of objects at a junction, in accordance with an exemplary aspect of the present disclosure.

To facilitate understanding, like reference numerals have been used, where possible to designate like elements common to the figures. DETAILED DESCRIPTION

The aspects herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting aspects that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the aspects herein. The examples used herein are intended merely to facilitate an understanding of ways in which the aspects herein may be practiced and to further enable those of skill in the art to practice the aspect herein. Accordingly, the examples should not be construed as limiting the scope of the aspect herein. FIG. 1 illustrates a block diagram of a collision avoidance system 100, in accordance with an aspect of the present disclosure. The collision avoidance system 100 may be an automated system that may be configured to mitigate and/or avoid collisions at a junction 202 (as shown in FIG. 2). The collision avoidance system 100 may include a plurality of detection units of which a detection unit 102 is shown, processing circuitry 104, a plurality of traffic control peripherals of which a traffic control peripheral 106 is shown, and a database 108. The detection unit 102, the processing circuitry 104, the traffic control peripherals 106, and the database 108 may be communicatively coupled by way of a communication network 110. In other aspects, the detection unit 102, the processing circuitry 104, the traffic control peripheral 106, and the database 108 can be communicably coupled through separate communication networks established therebetween. In some aspects of the present disclosure, the database 108 and the traffic control peripherals 106 may be directly coupled to the processing circuitry 104.

The communication network 110 may include suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for transmission and reception of data related to operations of various entities (such as the detection unit 102, the processing circuitry 104, the traffic control peripheral 106, and the database 108) of the collision avoidance system 100. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPV4) (or an IPV6 address) and the physical address may be a Media Access Control (MAC) address. The communication network 110 may be associated with an application layer for implementation of communication protocols based on one or more communication requests from the detection unit 102, the processing circuitry 104, the traffic control peripheral 106, and the database 108. The communication data may be transmitted or received, via the communication protocols. Examples of the communication protocols may include, but are not limited to, Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), Simple Mail Transfer Protocol (SMTP), Domain Network System (DNS) protocol, Common Management Interface Protocol (CMIP), Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Fong Term Evolution (FTE) communication protocols, or any combination thereof. In one aspects, the communication data may be transmitted or received via at least one communication channel of a plurality of communication channels in the communication network 110. The communication channels may include, but are not limited to, a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a data standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Aspects of the present disclosure are intended to include or otherwise cover any type of communication channel, including known, related art, and/or later developed technologies.

The detection unit 102 may be installed on all pathways leading to the junction 202. The detection unit 102 may be configured to detect a plurality of objects 200a-200n (as shown later in FIG. 2) moving towards the junction 202. The detection unit 102 may be further configured to sense signals representing (i) a direction of motion of each object of the plurality of objects 200a-200n and (ii) a speed of each object of the plurality of objects 200a-200n. The detection unit 102 may be further configured to classify each object of the plurality of objects 200a-200n as one of, a human and a machine. In some aspects of the present disclosure, the detection unit 102 may be configured to further identify a category of the machine based on the sensed signals. Specifically, the detection unit 102 may be configured to, based on the sensed signals classify the machine as one of, a car, a truck, a dumper, a bike, and the like. Aspects of the present disclosure are intended to include and/or otherwise cover any type of the classification for the machine, known to a person of ordinary skill in the art.

The processing circuitry 104 may be coupled with the detection unit 102 by way of the communication network 110. The processing circuitry 104 may be configured to receive the sensed signals from the detection unit 102. The processing circuitry 104 may be further configured to determine a probability of collision for each object of the plurality of objects 200a-200n based on the sensed signals. The processing circuitry 104 may be configured to determine a probability of collision for each object of the plurality of objects 200a-200n. Specifically, the processing circuitry 104 may be configured to determine the probability based on the sensed signals and the classification generated by the detection unit 102. The processing circuitry 104 may be further configured to determine the probability for (i) a human-machine collision and (ii) a machine-machine collision. Further, the processing circuitry 104 may be configured to actuate the traffic control peripheral 106 to pass an object of the plurality of objects 200a-200n through the junction 202 based on the determined probability. Examples of the traffic control peripheral 106 may include, but is not limited to, a Light Emitting Diodes (LED) based peripheral, a drop-gate, a boom-barrier, an alarm and/or siren-based traffic control peripheral, and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the traffic control peripheral 106 including known, related art, and/or later developed technologies.

The processing circuitry 104 may be further configured to generate historic data associated with each event of collision avoidance. The historic data may include one of, date of an event of collision avoidance, a time of the event of collision avoidance, each object of the plurality of objects 200a-200n involved, information pertaining to the pathway from which each object of the plurality of objects 200a-200n involved is approaching the junction 202, and the junction 202 involved. The traffic control peripheral 106 in FIG.l may be directly coupled with the processing circuitry 104 by way of the communication network 110. The traffic control peripheral 106 may be configured to pass the detected object through the junction 202 having higher priority based on the data received by the processing circuitry 104.

The database 108 may be configured to store the historic data associated with each event of collision avoidance and detection data for feedback, monitoring and training purposes. The detection data may include, but is not limited to, incoming and outgoing sensed objects of the plurality of objects 200a-200n at the junction 202, classification of the sensed objects as human and/or machine, type of the machine, speed of the sensed object, timestamp associated with a sensing of the object, and a time of arrival of each object of the plurality of objects 200a-200n at the junction 202. According to aspects of the present disclosure, the detection data may be utilized by the collision avoidance system 100 to monitor traffic flow, identify traffic violations and for future traffic planning purposes. Examples of database 108 may include, but is not limited to, a centralized database, a distributed database, a relational database, a NoSQL database, a cloud database, an object-oriented database, a hierarchical database, a network database, and the like. Aspects of the present disclosure are intended to include or otherwise cover any type of the database 108 including known, related art, and/or later developed technologies.

The collision avoidance system 100 may further include a real time clock 112 that may be coupled to the processing circuitry 104. The real time clock 112 may be configured to provide the date of the event of collision avoidance and the time of the event of collision avoidance to the processing circuitry 104 such that the processing circuitry 104 generates the historic data associated with each event of collision avoidance.

The detection unit 102 may include devices such as sensors 114a-114n of which the sensors 114a, 114b, 114c, 114d are shown, control circuitry 116, and a first antenna 118. As discussed, the detection unit 102 may be configured to detect all the objects moving in the direction of junction 202 and may be installed on all pathways leading to the junction 202. The sensors 114a, 114b, 114c, 114d in the detection unit 102 may be configured to sense the signals that represent the data pertaining to a direction of motion and speed of each object of the plurality of objects 200a-200n based on the sensed signals. The control circuitry 116 may be coupled to the sensors 114a, 114b, 114c, 114d and configured to receive the detection data and sensed signals from each sensor of the plurality of sensors 114a-l 14n. Further, the control circuitry 116 may be configured to classify each object of the plurality of objects 200a-200n of which first and second objects 200a and 200b are shown in FIG. 2, as one of, the human and the machine based on the sensed signal. In some aspects of the present disclosure, the control circuitry 116 may be configured to further identify the category of the machine based on the sensed signals. Specifically, the detection unit 102 may be configured to, based on the sensed signals classify the machine, as one of, a car, a truck, a dumper, a bike, and the like.

The control circuitry 116, in the detection unit 102 may be further configured to transmit the detection data and sensed signals to the processing circuitry 104 when an object of the plurality of objects 200a-200n is within a predefined distance from the junction 202. In some aspects of the present disclosure, the control circuitry 116 may be configured to transmit the detection data and sensed signals to the processing circuitry 104 by way of the communication network 110. In some other aspects of the present disclosure, the control circuitry 116 may be configured to transmit the detection data and sensed signals to the processing circuitry 104 by way of the first antenna 118. Specifically, the first antenna 118 may enable data transmission from the detection unit 102 to the processing circuitry 104. The first antenna 118 may include, but is not limited to, a horn antenna, a parabolic antenna, and the like. Aspects of the present disclosure are intended to include and/or otherwise cover any type of the first antenna 118, including known, related and later developed technologies. The processing circuitry 104 may include suitable circuitry to perform one or more operations. For example, the processing circuitry 104 may be configured to process and monitor the signals received from the detection unit 102. The processing circuitry 104 receive the data from the detection unit 102 and process that detection data accordingly. The processing circuitry 104 may be coupled to the second antenna 120, and configured to assign a priority to each object of the plurality of objects 200a-200n, such that the processing circuitry 104 actuates the traffic control peripheral and 106a, 106b, 106c, 106d (as shown in FIG. 2), by way of a plurality of actuators 122a, 122b, 122c, 122d (as shown in FIG. 2) to pass the object of the plurality of objects 200a-200n, through the junction 202, based on the priority assigned to object of the plurality of objects 200a-200n to avoid collision. The processing circuitry 104 may be mounted on a pole, typically in the corner and/or at the centre of the junction 202 such that the processing circuitry 104 receives information from the detection unit 102 by way of the second antenna 120. The second antenna 120 may enable data reception at the processing circuitry 104. The second antenna 120 may include, but is not limited to, a hom antenna, a parabolic antenna, and the like. Aspects of the present disclosure are intended to include and/or otherwise cover any type of the second antenna 120, including known, related and later developed technologies.

The processing circuitry 104 may be configured to process the data received from the detection unit 102, in one format and convert the data to machine readable format. Further, the processing circuitry 104 may be configured to trigger the actuators 122a, 122b, 122c, 122d to actuate the traffic control peripherals 106a, 106b, 106c, 106d when two or more objects are approaching the junction 202 that possesses a risk of man-machine or machine-machine collision. The processing circuitry 104 may be configured to trigger the actuators 122a, 122b, 122c, 122d to actuate the traffic control peripherals 106a, 106b, 106c, 106d in a manner that allows only one of the detected object to pass through the junction 202 while all the others are stopped, and one after another all the objects are allowed to pass through the junction 202, thus any possibility of human-machine or machine-machine interaction is restrained. In other aspects, when the processing circuitry 104 determines no risk of human-machine or machine-machine collision, the [processing circuitry 104 allows, by way of the traffic control peripherals 106a, 106b, 106c, 106d, the detected objects to pass through the junction 202 one by one without any interruption.

The processing circuitry 104 may be further configured to generate historic data that relate to each event of collision avoidance. The historic data comprises at least one of, a date of an event of collision avoidance, a time of the event of collision avoidance, each object of the plurality of objects 200a-200n involved, the junction 202 involved, information pertaining to the pathway from which each object of the plurality of objects 200a-200n involved is approaching the junction 202. Further, the historic data is stored in database 108, configured to store data associated with each event of collision avoidance for feedback, monitoring and training purposes. FIG. 2 illustrates a schematic view of a setup 201 of the collision avoidance system 100, in accordance with an exemplary aspect of the present disclosure. As illustrated in FIG. 2, the detection unit 102 is installed on all the pathways such as a pole 2 shown in FIG. 2 and configured to detect all the objects moving in the direction of junction 202. In some aspects of the present disclosure, the junction 202 may be, but not limited to, an intersection, a U-turn, and the like. In one aspect, the junction 202 may be a n- way junction such that the traffic control peripherals 106a-106n may be installed at the corresponding pathway of the n-way junction. Aspects of the present disclosure are intended to include and/or otherwise cover any type of the junction 202. The sensors 114 of which four sensors 114a, 114b, 114c and 114d are shown may include sensors such as, but not limited to, a Radar, an Ultrasonic sensor, an Infrared (IR) sensor, an imaging device, an image sensor, and the like. Aspects of the present disclosure are intended to include and/or otherwise cover any type of the sensors 114, including known, related and later developed technologies. The sensors 114a, 114b, 114c and 114d may be configured to sense the signal as data and pass the sensed data to the controller circuitry 114, the controller circuitry 114 further monitors that data and detect an object such as the first object 200a and the second object 200b as one of the human and machine and transmits the classification of the objects to the processing circuitry 104. The processing circuitry 104 installed at a pole 1 as shown may be configured to process the data received data from the detection unit 102 as discussed in conjunction with FIG. 1. The processing circuitry 104 may further actuate the traffic control peripherals 106 by way of the actuators 122a, 122b, 122c and 122d shown in FIG. 2 when the condition of collision arises. The control peripherals 106a, 106b, 106c and 106d may allow only detected first object 200a and second object 200b to pass through the junction 202 that has high priority based on the detected data while stopping other objects approaching the junction 202. The priority order of the detected objects 200a-200n may be decided by the junction 202 conditions. The priority order of the detected first object 200a and second object 200b may be determined by detecting the speed of each of the plurality of objects 200a-200n, a distance of each of the plurality of objects 200a-200n from the junction 202, and a time of arrival of each of the plurality of objects 200a-200n at the junction 202.

The real time clock 112 coupled to the processing circuitry 104 may be configured to provide the exact date of such event of collision avoidance and the time of the event of collision avoidance to the processing circuitry 104 such that the processing circuitry 104 generates the historic data. The processing circuitry 104 may be further configured to store the historic data, the detection data, and any data associated with the collision avoidance system 100 in a memory storage (not shown) such as, but not limited to a Read-Only Memory (ROM), a Random Access Memory (RAM), a flash memory, a removable storage drive, a hard disk drive (HDD), a solid-state memory, a magnetic storage drive, a Programmable Read Only Memory (PROM), an Erasable PROM (EPROM), and/or an Electrically EPROM (EEPROM). Aspects of the present disclosure are intended to include or otherwise cover any type of the memory storage including known, related art, and/or later developed memories to store data.

The detection unit 102 detects all the sense signals representing (i) a direction of motion of each object of the plurality of objects 200a-200n and (ii) a speed of each object of the plurality of objects 200a-200n. The detection unit 102 then classify each object of the plurality of objects 200a-200n as one of, a human and a machine and category of the machine based on the sensed signals. The controller circuitry 116 takes signals and processes these signals to generate data as a direction of motion of each object of the plurality of objects 200a-200n and (ii) a speed of each object of the plurality of objects 200a-200n. The detection data is then transmitted to the processing circuitry 104 for processing and monitoring by way of the first antenna 118. The actuators 122a ,122b, 122c and 122d actuates the traffic control peripherals 106a, 106b, 106c and 106d when the condition for collision arises based on the data received by processing circuitry 104.

FIG. 3 illustrates a method 300 for collision avoidance at the junction 202. At step 302, the detection data and sensed signals is received by the control circuitry 116 of the detection unit 102. The sensed signals may represent (i) a direction of motion of each object of the plurality of objects 114a-114n and (ii) a speed of each object of the plurality of objects 200a-200n from each sensor of a plurality of sensors 114a- 114n. At step 304, each object of the plurality of objects 200a-200n is classified as one of, the human and the machine, and further category of the machine may be determined based on the sensed signals by the controller circuitry 116. At step 306, the detection data is transmitted to the processing circuitry 104, when an object of the plurality of objects 200a-200n is within a predefined distance from the junction 202. The detection data may include a distance of the object from the junction 202 and the classification of the object. At step 308, a probability of collision for each object 200a-200b of the plurality of objects 200a-200n based on the sensed signals and the classification is determined by the processing circuitry 104. At step 310, priority to each detected objects 200a- 200b is assigned by the processing circuitry 104. At step 312, a traffic control peripheral 106 to pass an object 200a-200b of the plurality of objects 200a-200n through the junction 202 based on the determined probability is actuated by the processing circuitry 104.

As will be readily apparent to those skilled in the art, the present aspect may easily be produced in other specific forms without departing from its essential characteristics. The present aspects are therefore, to be considered as merely illustrative and not restrictive, the scope being indicated by the claims rather than the foregoing description, and all changes which come within therefore intended to be embraced therein.

Claims

WE CLAIM

1. A collision avoidance system (100) comprising: a detection unit (102) installed on pathways leading to a junction (202) to monitor a plurality of objects (200a-200n) approaching the junction (202) , wherein the detection unit (102) is configured to sense signals representing (i) a direction of motion of each object of the plurality of objects (200a-200n) and (ii) a speed of each object of the plurality of objects (200a-200n), wherein the detection unit (102) is configured to classify each object of the plurality of objects (200a-200n) as one of, a human and a machine based on the sensed signals such that based on the sensed signals, the detection unit (102) further classifies the machine as one of, a car, a truck, a dumper, and a bike; and processing circuitry (104) coupled to the detection unit (102), and configured to: receive the sensed signals from the detection unit (102); determine a probability of collision for each object of the plurality of objects (200a-200n) based on the sensed signals and the classification, wherein the probability is determined for (i) a human-machine collision and (ii) a machine- machine collision; and actuate a traffic control peripheral (106) to pass an object of the plurality of objects (200a-200n) through the junction (202) based on the determined probability.

2. The collision avoidance system (100) as claimed in claim 1, wherein the detection unit (102) further comprises a plurality of sensors (114a-114n) configured to sense signals representing (i) the direction of motion of each object of the plurality of objects (200a-200n) and (ii) the speed of each object of the plurality of objects (200a-200n). 3. The collision avoidance system (100) as claimed in claim 2, wherein the detection unit (102) further comprises control circuitry (116) that is coupled to the plurality of sensors (114a-114n), and configured to: receive the sensed signals from each sensor of the plurality of sensors (114a-

114n); classify each object of the plurality of objects (200a-200n) as one of, the human and the machine, based on the sensed signals such that based on the sensed signals, the machine is classified as one of, a car, a truck, a dumper, and a bike; and transmit detection data and the sensed signals to the processing circuitry (104) when an object of the plurality of objects (200a-200n) is within a predefined distance from the junction (202), wherein the detection data comprises a distance of the object from the junction (202) and the classification of the object.

5. The collision avoidance system (100) as claimed in claim 1, wherein the processing circuitry (104) is further configured to assign a priority to each object of the plurality of objects (200a-200n) such that processing circuitry (104) actuates the traffic control peripheral (106) to pass an object of the plurality of objects (200a-200n) through the junction (202) based on the priority assigned to the object (200a-200b) to avoid collision.

6. The collision avoidance system (100) as claimed in claim 1, wherein the processing circuitry (104) is further configured to generate historic data associated with each event of collision avoidance, wherein the historic data comprises at least one of, a date of an event of collision avoidance, a time of the event of collision avoidance, each object of the plurality of objects (200a-200n) involved, and the junction (202) involved.

7. The collision avoidance system (100) as claimed in claim 6, further comprising a database (108) configured to store the historic data associated with each event of collision avoidance and detection data for feedback, monitoring and training purposes.

8. The collision avoidance system (100) as claimed in claim 6, further comprising a real time clock (112) that is coupled to the processing circuitry (104), and configured to provide the date of the event of collision avoidance and the time of the event of collision avoidance to the processing circuitry (108). 9. The method (300) for collision avoidance at a junction (202), the method comprising: receiving, by control circuitry (116) of a detection unit (102), sensed signals representing (i) a direction of motion of each object of the plurality of objects (114a- 114n) and (ii) a speed of each object of the plurality of objects (200a-200n) from each sensor of a plurality of sensors (114a-l 14n); classifying, by the control circuitry (116), each object of the plurality of objects (200a-200n) as one of, the human and the machine, based on the sensed signals such that based on the sensed signals, the control circuitry (116) further classifies the machine as one of, a car, a truck, a dumper, and a bike; transmitting, by the control circuitry (116), detection data to processing circuitry (104), when an object of the plurality of objects (200a-200n) is within a predefined distance from the junction (202), wherein the detection data comprises a distance of the object from the junction (202) and the classification of the object; determining, by the processing circuitry (104), a probability of collision for each object (200a-200b) of the plurality of objects (200a-200n) based on the sensed signals and the classification, wherein the probability is determined for (i) a human- machine collision and (ii) a machine-machine collision; assigning, by the processing circuitry (104), a priority to each objects (200a- 200b) for collision of the plurality of objects (200a-200n) based on the sensed signals and the classification. actuating, by the processing circuitry (104), a traffic control peripheral (106) to pass an object (200a-200b) of the plurality of objects (200a-200n) through the junction (202) based on the determined probability.

10. The method (300) as claimed in claim 9, further comprising assigning, by the processing circuitry (104), a priority to each object of the plurality of objects (200a- 200n) such that processing circuitry (104) actuates the traffic control peripheral (106) to pass an object (200a-200b) of the plurality of objects (200a-200n) through the junction (202) based on the priority assigned to the object (200a-200b) to avoid collision.