Description:FIELD OF THE INVENTION
[001] The present invention relates to a vehicle. More particularly, the present invention relates to a system and method for estimating a profile of a surface under the vehicle in a driving state of the vehicle.

BACKGROUND OF THE INVENTION
[002] In conventional vehicles, systems and methods are provided to detect a profile of a surface such as a road surface. The surfaces are bound to be damaged over time due to wear and tear caused by reasons such as movement of the vehicles, weather conditions etc. In order to prevent damage to the vehicles riding over the damaged surfaces, various surface profile detection techniques have been employed in the prior arts. The techniques may be broadly categorized into three types namely, three-dimensional reconstruction, vision-based methods, and vibration-based methods.
[003] In the three-dimensional reconstruction technique, the vehicle captures images of the surface to generate accurate analysis of anomalies like potholes, cracks, and the like. In one example, the vehicle comprises three-dimensional laser scanners that reflect laser beams from the surface to generate images and detect features like presence, size, and depth of the anomalies. However, the laser scanners are extremely expensive and economically un-viable for equipping with the conventional vehicles. In another example, the vehicle comprises multiple digital cameras to identify and classify cracks from two-dimensional images. The two-dimensional images are used to recreate three-dimensional images. However, such recreation requires significant computational power for camera calibration, distortion adjustment, feature matching, and three-dimensional reconstruction and is not feasible to be carried out in real time. In yet another example, a Kinect sensor is used. The Kinect sensor is a motion sensing device that is used along with a camera to generate three-dimensional images of the anomalies. The Kinect sensor is mounted on the vehicle. However, low accuracy and high computational power of the Kinect sensor makes it undesirable
[004] The vision-based techniques are two-dimensional image-based and video-based methods. The addition of advanced driver assistance systems in vehicles has led to an increase in camera usage for vehicles. In the image-based method, an algorithm is trained to identify the features of the anomalies by differentiating between pixels of light intensity, graining and colour. However, the image-based technique has low accuracy and high computational power which is undesirable. In the video-based method, an algorithm is trained to identify the features of the anomalies of a continuous stream of images by differentiating between pixels of light intensity, graining and colour. The video-based technique requires high end cameras and executing the algorithm in local and inexpensive cameras is not possible which is undesirable.
[005] The vibration-based methods detect forces on the vehicles caused by unevenness of the surfaces in the form of vibrations. The conventional vehicles identify patterns of series of the vibrations to estimate the profile of the surface. The vehicles include an accelerometer and a speed sensor that help in sensing the vibrations and estimating the profile. However, the vibrations may represent false data caused due to turning, braking, and engine processes of the vehicles which is undesirable for accurate estimation of the profile.
[006] In another estimation technique of the profile of the surface, time domain information of the speed sensor, the accelerometer, and an axle height sensor regarding the movement of the vehicle is utilized. Based on the time domain information, a chassis vertical movement is determined by the vehicle that leads to ascertaining the anomalies of the surface. However, the time domain information does not reveal frequency domain information regarding the movement of the vehicle which is critical to accurately predict the anomalies with different payloads on the vehicle. As a result, the real time prediction of the profile of the surface using only time domain information may be inaccurate which is undesirable.
[007] In view thereof, there is a need-felt to overcome at least the above-mentioned disadvantages of the prior art.
SUMMARY OF THE INVENTION
[008] In one aspect of the present invention, a system for estimating a profile of a surface is disclosed. The system comprises one or more speed sensors, one or more accelerometers, a global positioning system sensor and a control unit. The one or more speed sensors, the one or more accelerometers, the global positioning system (GPS) sensor and the control unit are mounted on the vehicle. The one or more speed sensors, the one or more accelerometers, the GPS sensor are communicatively coupled to the control unit. The one or more speed sensors are configured to detect an absolute speed of the vehicle. The one or more accelerometers are configured to detect an acceleration of the vehicle. The GPS sensor is configured to detect a location of the vehicle. The control unit is configured to receive signals indicative of the absolute speed, the acceleration and the location of the vehicle. Upon receiving the signals, the control unit is further configured to process the signals indicative of the absolute speed and acceleration to generate at least a first set of pre-defined parameters. The first set of pre-defined parameters are based on both time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration. Based on at least the first set of pre-defined parameters, the control unit determines a surface profile index for the detected location and compares the determined surface profile with one or more pre-defined surface profile indices to classify the surface into one of a plurality of pre-defined levels. The plurality of pre-defined levels indicates conditions of the surface. The control unit is further configured to transmit information indicative of the classified level of the surface to one or more devices.
[009] In another aspect of the present invention, a method for estimating a profile of a surface is disclosed. The method comprises a step of detecting an absolute speed of the vehicle. The step of the detecting the absolute speed of the vehicle is performed by one or more speed sensors mounted on the vehicle. The method further comprises a step of detecting an acceleration of the vehicle. The step of detecting the acceleration is performed by one or more accelerometers mounted on the vehicle. The method further comprises a step of detecting a location of the vehicle. The step of detecting the location of the vehicle is performed by a global positioning system (GPS) sensor mounted on the vehicle. The method further comprises a step of receiving signals indicative of the absolute speed, the acceleration and the location of the vehicle. The step of receiving signals indicative of the absolute speed, the acceleration and the location of the vehicle is performed by a control unit mounted on the vehicle. The method further comprises a step of processing the signals indicative of the absolute speed and the acceleration to generate at least a first set of pre-defined parameters. The step of processing the signal is performed by the control unit. The first set of pre-defined parameters are based on time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration. The method further comprises a step of determining a surface profile index for the detected location based on at least the first set of pre-defined parameters. The step of determining is performed by the control unit. The method further comprises a step of comparing the determined surface profile index with one or more pre-defined surface profile indices to classify the surface into one of a plurality of pre-defined levels. The plurality of pre-defined levels indicates conditions of the surface. The step of comparing is performed by the control unit. The method further comprises a step of transmitting information indicative of the classified level of the surface to one or more devices. The step of transmitting is performed by the control unit.

BRIEF DESCRIPTION OF THE DRAWINGS
[010] Reference will be made to embodiments of the invention, examples of which may be illustrated in accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
Figure 1 and Figure 2 are block diagrams illustrating a system for estimating a profile of a surface, in accordance with an embodiment of the present invention.
Figure 3 is pictorial representation illustrating various level of the surfaces, in accordance with the embodiment of the present invention.
Figure 4 is a flow chart illustrating a method for estimating profile of the surface, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION
[011] Various features and embodiments of the present invention here will be discernible from the following further description thereof, set out hereunder.
[012] Figure 1 and Figure 2 are block diagrams illustrating a system 100 for estimating a profile of a surface, in accordance with an embodiment of the present invention. More particularly, Figure 2 illustrates detailed construction of a control unit 102, in accordance with the embodiment of the present invention.
[013] The system 100 comprises one or more speed sensors 104, one or more accelerometers 106, a global positioning system (GPS) sensor 108 and the control unit 102. The one or more speed sensors 104, the one or more accelerometers 106, the global positioning system (GPS) sensor 108 and the control unit 102 are mounted on a vehicle 10. The term “vehicle” for the purpose of the present invention includes saddle type vehicle such as, not being limited to, such as bicycles, scooters, motorcycles, and the likes as well as passenger type vehicle such as, not being limited to, as auto rickshaws, cars, lorries, truck and the likes. The term “vehicle” also includes conventional internal combustion engine vehicles, electric vehicles, and hybrid vehicles.
[014] The one or more speed sensors 104 are configured to detect an absolute speed of the vehicle 10. In a non-limiting example, the one or more speed sensors 104 are mounted on a front wheel of the vehicle 10. In a non-limiting example, the one or more speed sensors 104 are selected from a group of hall effect sensors, optical sensors, magnetic inductive sensors and resistive sensors. The one or more accelerometers 106 are configured to detect an acceleration of the vehicle 10. In a non-limiting example, the one or more accelerometers 106 are mounted under a seat of the vehicle 10. In another non-limiting example, the one or more accelerometers 106 are mounted on a front axle of the vehicle 10. In a non-limiting example, the one or more accelerometers 106 are selected from a group of piezoelectric sensors, piezoresistive sensors and capacitive sensors. The GPS sensor 108 is configured to detect a location of the vehicle 10.
[015] The control unit 102 is communicatively coupled to the one or more speed sensors 104, the one or more accelerometers 106 and the GPS sensor 108. The control unit 102 is configured to receive signals indicative of the absolute speed, the acceleration and the location of the vehicle 10. Upon receiving the signals, the control unit 102 is configured to process the signal to generate at least a first set of pre-defined parameters. The first set of pre-defined parameters are based on time domain information and frequency domain information of absolute speed and acceleration. In one non-limiting example, the first set of pre-defined parameters comprises Continuous Wavelet Transform (CWT) of the acceleration and Continuous Wavelet Transform (CWT) of the absolute speed. Based on the first set of pre-defined parameters, the control unit 102 is configured to determine a surface profile index for the detected location. The control unit 102 is further configured to compare the determined surface profile index with one or more pre-defined surface profile indices to classify the surface into one of the plurality of pre-defined levels. In one non-limiting example, the plurality of pre-defined levels comprises a first level L1, a second level L2 and a third level L3, as shown in Figure 3. The first level L1 is assigned to new or well-maintained surfaces with minor imperfection. The second level is assigned to damaged surfaces/pavement or cemented surfaces/roads with cracks, rutting and minor depressions. The third level is assigned to heavily damaged pavements/surfaces or cemented surfaces/roads with potholes and humps, and unpaved mud roads. The number of levels defined herein should not be construed as limiting and more or less levels can be defined as per the requirements of the manufacturer of the vehicle 10 or the surface conditions of the road on which the vehicle 10 is intended to travel. On determination of the classified level, the control unit 102 is configured to transmit the classified level of the surface to one or more devices 112. In one non-limiting example, the one or more devices 112 comprises a display unit 114 of the vehicle 10, a personal digital assistant 116 of the rider, one or more remote servers 118 and/or an IoT device 120.
[016] In an embodiment, the control unit 102 is configured to generate a second set of pre-defined parameters. The second set of pre-defined parameters are generated to determine the surface profile index of the surface with increased accuracy. In one non-limiting example, the second set of pre-defined parameters comprises average vehicle speed, mean and power of the signals indicative of the acceleration of the vehicle 10, occupied bandwidth of acceleration and/or upper frequency limit of the signals indicative of the acceleration.
[017] In an embodiment, as shown in Figure 2, the control unit 102 comprises a data acquisition unit 122, a prediction unit 124 and a logging unit 126. The data acquisition unit 122 is configured to initialize the one or more speed sensors 104, the one or more accelerometers 106 and the GPS sensor 108. The data acquisition unit 122 is further configured to receive the signals indicative of the absolute speed, the acceleration, and the location of the vehicle 10 from the one or more speed sensors 104, the one or more accelerometers 106 and the GPS sensor 108. The data acquisition unit 122 is further configured to transmit the received signal to the prediction unit 124 and the logging unit 126.
[018] The prediction unit 126 is configured to receive signal from the data acquisition unit 122. The prediction unit 126 comprises a signal processor unit 128, a profile index unit 130 and a profile estimation unit 132. The signal processor unit 128 is configured to process the signals indicative of the absolute speed and the acceleration to generate at least the first set of pre-defined parameters. In an embodiment, the signal processor unit 128 may also generate the second set of pre-defined parameters. The profile index unit 130 is configured to receive the first and second sets of pre-defined parameters from the signal processor unit and determine the surface profile index for the detected location based on the first and second sets of pre-defined parameters. The profile estimation unit 132 is configured to receive and compare the determined surface profile index with the one or more pre-defined surface profile indices to classify the surface as one of the plurality of pre-defined levels and transmit the information indicative of the classified level of the surface to the logging unit 126. The logging unit 126 is configured to receive and store the information indicative of the classified level of the surface from the prediction unit 124. The logging unit 126 is further configured to receive and store the signals indicative of the absolute speed, the acceleration, and the location of the vehicle 10 from the data acquisition unit 122. The logging unit 126 is further configured to transmit the stored information and the stored signals to the one or more devices 112.
[019] It is to be understood that the plurality of pre-defined levels is pre-fed in the control unit 102. The plurality of pre-defined levels is ascertained by a training process which includes selection of training routes such that the selected training route comprises a mixture of surfaces representing the plurality of pre-defined levels. To account for different rider weights, the selected route is ridden by different riders having different weights. When the selected route is ridden by different riders having different weights, necessary data such as speed of the vehicle, acceleration of the vehicle, location of the vehicle etc. are collected by different sensors mounted on the vehicle 10 and processed by a processing unit (in the training process) to determine surface profile index using the following formula:
Surface profile index= (power (acc)*std (acc)) / average speed of the vehicle in the detection window
wherein: power (acc) is power in the acceleration signal and std (acc) is standard deviation in the acceleration signal.
[020] It is to be understood that power in an acceleration signal is directly proportional to roughness of the surface. Rougher surfaces will result in larger amplitudes of acceleration and, therefore, higher power in the signal. However, at the same time, for a given surface, the power in the acceleration signal is also related to speed. A higher speed will result in a higher power for the same surface. Therefore, the surface roughness is related to the power of the acceleration signal for a certain given speed. However, if just power to speed ratio is taken, the metric becomes overly sensitive to speed as for low speed the metric will take on very high values. Also, just taking power to speed ratio cannot distinguish between braking due to traffic and braking due to an actual change in the surface. Therefore, standard deviation of acceleration is also considered in calculation of surface profile index. If a braking event occurred due to traffic, there would be no change in road surface and the standard deviation of the acceleration signal would be low. If there was a change in road surface resulting in a change in vertical acceleration, the standard deviation would be high. At low speeds, the standard deviation of the acceleration signal will also be low. At low speeds, the low standard deviation negates the effect of the decreasing denominator by decreasing the numerator as well and when the change in speed is due to a change in surface, the high standard deviation allows the surface profile index value to rise appropriately.
[021] It is to be understood that the above-mentioned formula used in the training process to determine plurality of the pre-defined surface profile indices is also used by the control unit 102 for determination of surface profile index of the surface in real time which is compared with the plurality of pre-defined surface profile indices (defined during training process and pre-fed in control unit) to classify the surface into one of the plurality of pre-defined levels.
[022] It is also to be understood that signals indicative of speed and acceleration must be fused and processed such that they can be used by control unit 102 or processing unit (used during training process) to predict the surface. Both these inputs are time domain data, and they cannot be directly used by the control unit 102 or the processing for determining the surface profile index as they are random signals. Therefore, there is a need for time as well as frequency domain information which can be given as the input to the control unit 102. This can be achieved by using Continuous wavelet transformations (CWT). The frequency information with respect to time, amplitude information with respect to time and frequency can be inferred from this transformation and a plurality of pre-defined surface profile indices as well as surface profile index in real time can be generated. In one non-limiting example, the frequency of peaks for first level L1 occur mostly between 20-30 Hz, for second level L2 occurs mostly between 10-25 Hz and for third level L3 occur mostly between 7-12Hz. From this it can be inferred that there are different peak frequencies in the signal depending on the road levels. Further the magnitude of peaks infers the following result: for first level L1, the peak magnitude is 0.6, for second level L2, the peak magnitude is 0.9 and for third level L3, the peak magnitude is 1.5. The frequency of peaks for potholes as well as speed breakers generally form a part of the second level and the third level. This is because rougher roads will impart higher acceleration to the vehicle 10 for a given speed.
[023] In an embodiment, the control unit 102 is configured to generate and transmit a warning signal to the one or more devices 112 on detection of the second level of surface or the third level of surface. The warning signals are generated by an audio alert device, a visual alert device and/or a haptic alert device. The audio alert device can be a buzzer, a horn and/or a speaker. The buzzer can be a magnetic or a piezoelectric buzzer. The audio alert device can be mounted on a handlebar of the vehicle 10, an engine of the vehicle 10, a fuel tank of the vehicle 10 and/or a suspension of the vehicle 10. The visual alert can be one or more light emitting diodes provided at location which are easily viewable by rider in running states of the vehicle such as handlebar assembly, housing of a rear-view mirror etc. The haptic alert device can be mounted on a fuel tank, a handlebar, a seat occupied by the rider of the vehicle, foot pegs on which the rider places his feet or floorboard on which the rider places his feet.
[024] In an embodiment, the control unit 102 is configured to suggest an alternate route having a level less than the classified level of the surface when the classified level of surface is the second level of surface or the third level of the surface.
[025] In an embodiment, the control unit 102 is configured to control a suspension, a throttle and/or a brake of the vehicle based on the information indicative of the classified level of the surface. The term “control” here refers to an operation performed by the control unit to limit the speed of the vehicle such as implementing adaptive cruise control or arrest the speed of the vehicle by performing emergency braking operations. Such controls are generally implemented when the level of the surface is classified as second level or third level. Also, such controls are implemented after receiving approval/permission from the rider of the vehicle 10.
[026] Figure 4 is a flow chart illustrating a method 400 for estimating the profile of the surface, in accordance with an embodiment of the present invention.
[027] At step 402, the method comprises detecting an absolute speed of the vehicle 10. The step 402 of detecting the absolute speed of the vehicle 10 is performed by one or more speed sensors 104 mounted on the vehicle 10. At step 404, the method comprises detecting an acceleration of the vehicle 10. The step 404 of detecting the acceleration is performed by one or more accelerometers 106 mounted on the vehicle 10. At step 406, the method comprises detecting a location of the vehicle 10. The step 406 of detecting the location of the vehicle 10 is performed by a global positioning system (GPS) sensor 108 mounted on the vehicle 10. At step 408, the method comprises receiving signals indicative of the absolute speed, the acceleration and the location of the vehicle 10. The step 408 of receiving signals indicative of the absolute speed, the acceleration and the location of the vehicle is performed by a control unit 102 mounted on the vehicle 10. At step 410, the method comprises processing the signals indicative of the absolute speed and the acceleration to generate at least a first set of pre-defined parameters. The step 410 of processing the signal is performed by the control unit 102. The first set of pre-defined parameters are based on time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration. In one non-limiting example, the first set of pre-defined parameters comprises Continuous Wavelet Transform of the acceleration and Continuous Wavelet Transform of the absolute speed. At step 412, the method comprises determining a surface profile index for the detected location based on at least the first set of pre-defined parameters. The step 412 of determining the surface profile index is performed by the control unit 102. At step 414, the method comprises comparing the determined surface profile index with one or more pre-defined surface profile indices to classify the surface into one of a plurality of pre-defined levels. The plurality of pre-defined levels indicates conditions of the surface. In one non-limiting example, the plurality of pre-defined levels comprises a first level for well-maintained surfaces, a second level for damaged surfaces and a third level for severely damaged surfaces. The step 414 of comparing is performed by the control unit 102. At step 416, the method transmitting information indicative of the classified level of the surface to one or more devices 112. The step 416 of transmitting is performed by the control unit 102. In one non-limiting example, the one or more devices 112 comprises a display unit 114 of the vehicle 10, a personal digital assistant 116 of the rider, one or more remote servers 118 and/or an IoT device 120.
[028] In an embodiment, the method further comprises generating a second set of pre-defined parameters. The step of generating the second set of pre-defined parameters is performed by the control unit 102. In one non-limiting example, the second set of pre-defined parameters comprises average vehicle speed, mean and power of the signals indicative of the acceleration of the vehicle, occupied bandwidth of acceleration and/or upper frequency limit of the signals indicative of the acceleration.
[029] In an embodiment, the method further comprises a step of initializing the one or more speed sensors 104, the one or more accelerometers 106 and the GPS sensor 108. The step of initializing is performed by a data acquisition unit 122. The data acquisition unit 122 is part of the control unit 102. The method further comprises a step of receiving the signals indicative of the absolute speed, the acceleration, and the location of the vehicle 10. The step of receiving the signals indicative of the absolute speed, the acceleration, and the location of the vehicle 10 is performed by the data acquisition unit 122. The method further comprises a step of transmitting the received signals to the prediction unit 124 and the logging unit 126. The step of transmitting the received signals is performed by the data acquisition unit 122. The method further comprises a step of receiving the signals from the data acquisition unit 122. The step of receiving the signals from the data acquisition unit 122 is performed by a prediction unit 124. The prediction unit 124 is a part of the control unit 102. The method further comprises a step of processing the signals indicative of the absolute speed and the acceleration to generate the first and second sets of pre-defined parameters. The step of processing the signals indicative of the absolute speed and the acceleration is performed by a signal processor unit 128 of the prediction unit 124. The method further comprises a step of receiving the first and second sets of pre-defined parameters from the signal processor unit 128. The step of receiving the first and second sets of pre-defined parameters is performed by a profile index unit 130 of the prediction unit 124. The method further comprises a step of determining the surface profile index for the detected location based on the first and second sets of pre-defined parameters. The step of determining the surface profile index is performed by the profile index unit 130. The method further comprises a step of receiving and comparing the determined surface profile index with the one or more pre-defined surface profile indices to classify the surface as one of the plurality of pre-defined levels. The step of comparing is performed by a profile estimation unit 132 of the prediction unit 124. The method further comprises a step of transmitting the information indicative of the classified level of the surface to the logging unit 126. The step of transmitting the information indicative of the classified level of the surface is performed by the profile estimation unit 132. The method further comprises a step of receiving and storing the information indicative of the classified level of the surface from the prediction unit 124. The step of receiving and storing the information is performed by the logging unit 126. The logging unit 126 is a part of the control unit 102. The method further comprises a step of receiving and storing the signals indicative of the absolute speed, the acceleration, and the location of the vehicle 10 from the data acquisition unit 122. The step of receiving and storing the signal is performed by the logging unit 126. The method further comprises a step of transmitting the stored information and the stored signals to the one or more devices 112. The step of transmitting the stored information and the stored signals is performed by the logging unit 126.
[030] In an embodiment, the method comprises a step of generating and transmitting a warning signal to the one or more devices 112 on detection of the level of surface as the second level and the third level. The step of generating and transmitting the warning signal is performed by the control unit 102.
[031] In an embodiment, the method comprises a step of suggesting an alternative route having a level less than the classified level of the surface when the classified level of surface is the second level and the third level. The step of suggesting the alternative route is performed by the control unit 102.
[032] In an embodiment, the method further comprises a step of controlling a suspension, a throttle and/or a brake of the vehicle 10 based on the information indicative of the classified level of the surface. The step of controlling is performed by the control unit 102.
[033] It is to be understood that typical hardware configuration of the control unit 102 can include a set of instructions that can be executed to cause the control unit 102 to perform the above-disclosed operations.
[034] The control unit 102 may include a processor which may be a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor may be one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor may implement a software program, such as code generated manually i.e., programmed.
[035] The control unit 102 may include a memory. The memory may be a main memory, a static memory, or a dynamic memory. The memory may include but is not limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. The memory is operable to store instructions executable by the processor. The functions, acts or tasks illustrated in the figures or described may be performed by the programmed processor executing the instructions stored in the memory.
[036] Additionally, the control unit 102 may include an input device configured to allow a user to interact with any of the components of the control unit. The input device may be a number pad, a keyboard, or a cursor control device, such as a mouse, or a joystick, touch screen display, remote control or any other device operative to interact with the control unit 102.
[037] The control unit 102 may also include a disk or optical drive unit. The disk drive unit may include a computer-readable medium in which one or more sets of instructions, e.g., software, can be embedded. Further, the instructions may embody one or more of the methods or logic as described. In a particular example, the instructions may reside completely, or at least partially, within the memory or within the processor during execution by the control unit 102. The memory and the processor also may include computer-readable media as discussed above. The present invention contemplates a computer-readable medium that includes instructions or receives and executes instructions responsive to a propagated signal so that a device connected to a network can communicate data over the network. Further, the instructions may be transmitted or received over the network. The network may include wired networks, wireless networks, Ethernet AVB networks, or combinations thereof. The wireless network may be a cellular telephone network. Further, the network may be a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed.
[038] The claimed features/method steps of the present invention as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Specifically, the technical problem of accurately estimate a profile of the surface is solved by the present invention.
[039] In the present invention, the time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration are used for accurately estimating a profile of the surface.
[040] The present invention does not require use of additional devices like camera(s) for estimating profile of the surface. The present invention is performed by sensors which are already present in vehicle. The present invention is, therefore, cost effective and commercially viable. The present invention can function in poor visibility conditions such as night and bad weather. The present invention takes into conditions different weights of the rider during training process, making it more feasible demographically. The present invention classifies the level of the surface in real time. The present invention also warns the rider based on the estimated profile of the surface. The present invention also suggests alternative routes to a rider of the vehicle when the surface traversed by the vehicle is damaged. The alternative routes suggested by the system have a better profile than the current route being traversed by the vehicle. The present invention may also control the suspensions, the throttle and/or the brakes of the vehicle based on the classified level of the surface to avoid possibility of damage to the vehicle or to the rider of the vehicle, which may be caused by an accident.
[041] While the present invention has been described with respect to certain embodiments, it will be apparent to those skilled in the art that various changes and modification may be made without departing from the scope of the invention as defined in the following claims.
List of Reference Numerals:
10-vehicle
100-system
102-control unit
104-speed sensors
106-accelerometers
108-GPS sensor
112-devices
114-display unit
116-personal digital assistant
118-servers
120-IoT device
122-data acquisition unit
124-prediction unit
126-logging unit
128-signal processor unit
130-profile index unit
132-profile estimation unit
, Claims:1. A system (100) for estimating a profile of a surface, the system (100) comprising:
one or more speed sensors (104) mounted on a vehicle (10), the one or more speed sensors (104) being configured to detect an absolute speed of the vehicle (10);
one or more accelerometers (106) mounted on the vehicle (10), the one or more accelerometers (106) being configured to detect an acceleration of the vehicle (10);
a global positioning system (GPS) sensor (108) mounted on the vehicle (10), the GPS sensor (108) being configured to detect a location of the vehicle (10); and
a control unit (102) mounted on the vehicle (10) and communicatively coupled to the speed sensor (104), the accelerometer (106) and the GPS sensor (108), the control unit (102) being configured to:
receive signals indicative of the absolute speed, the acceleration and the location of the vehicle (10);
process the signals indicative of the absolute speed and the acceleration to generate at least a first set of pre-defined parameters, the first set of pre-defined parameters based on time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration;
determine a surface profile index for the detected location based on at least the first set of pre-defined parameters;
compare the determined surface profile index with one or more pre-defined surface profile indices to classify the surface into one of a plurality of pre-defined levels; and
transmit information indicative of the classified level of the surface to one or more devices (112).

2. The system (100) as claimed in claim 1, wherein the first set of pre-defined parameters comprises: Continuous Wavelet Transform of the acceleration and Continuous Wavelet Transform of the absolute speed.

3. The system (100) as claimed in claim 1, wherein the control unit (102) is configured to generate a second set of pre-defined parameters, the second set of pre-defined parameters generated to determine the surface profile index.

4. The system (100) as claimed in claim 3, wherein the second set of pre-defined parameters comprises at least one of: average vehicle speed, mean and power of the signals indicative of the acceleration of the vehicle, occupied bandwidth of acceleration and upper frequency limit of the signals indicative of the acceleration.

5. The system (100) as claimed in claim 1, wherein the plurality of pre-defined levels indicates conditions of the surface, the plurality of predefined levels comprises a first level for well-maintained surfaces, a second level for damaged surfaces and a third level for severely damaged surfaces.

6. The system (100) as claimed in claim 1, wherein the one or more devices (112) comprises at least one of: a display unit (114) of the vehicle (10), a personal digital assistant (116) of the rider, one or more remote servers (118) and an IoT device (120).

7. The system (100) as claimed in claim 4, wherein the control unit (102) comprises a data acquisition unit (122), a prediction unit (124) and a logging unit (126).

8. The system (100) as claimed in claim 7, wherein the data acquisition unit (122) is configured to:
initialize the one or more speed sensors (104), the one or more accelerometers (106) and the GPS sensor (108);
receive the signals indicative of the absolute speed, the acceleration, and the location of the vehicle (10); and
transmit the received signals to the prediction unit (124) and the logging unit (126).

9. The system (100) as claimed in claim 8, wherein the prediction unit (124) is configured to receive the signals from the data acquisition unit (122), the prediction unit (124) comprising:
a signal processor unit (128) configured to process the signals indicative of the absolute speed and the acceleration to generate the first and second sets of pre-defined parameters;
a profile index unit (130) configured to receive the first and second sets of pre-defined parameters from the signal processor unit (128) and determine the surface profile index for the detected location based on the first and second sets of pre-defined parameters; and
a profile estimation unit (132) configured to receive and compare the determined surface profile index with the one or more pre-defined surface profile indices to classify the surface as one of the plurality of pre-defined levels and transmit the information indicative of the classified level of the surface to the logging unit (126).

10. The system (100) as claimed in claim 9, wherein the logging unit (126) is configured to:
receive and store the information indicative of the classified level of the surface from the prediction unit (124);
receive and store the signals indicative of the absolute speed, the acceleration, and the location of the vehicle (10) from the data acquisition unit (122); and
transmit the stored information and the stored signals to the one or more devices (112), wherein the one or more devices (112) comprises at least one of: a display unit (114) of the vehicle (10), a personal digital assistant (116) of the rider, one or more remote servers (118) and an IoT device (120).

11. The system (100) as claimed in claim 5, wherein the control unit (102) is configured to generate and transmit a warning signal to the one or more devices (112) on detection of the classified level of the surface being the second level and the third level.

12. The system (100) as claimed in claim 5, wherein the control unit (102) is configured to suggest an alternative route having a level less than the classified level of the surface, the classified level being the second level and the third level.

13. The system (100) as claimed in claim 10, wherein the control unit (102) is configured to control at least one of a suspension (20), a throttle (30), and a brake (40) of the vehicle (10) based on the information indicative of the classified level of the surface.

14. A method (400) for estimating a profile of a surface, the method (400) comprising:
detecting (402), by one or more speed sensors (104) mounted on a vehicle (10), an absolute speed of the vehicle (10);
detecting (404), by one or more accelerometers (106) mounted on the vehicle (10), an acceleration of the vehicle (10);
detecting (406), by a global positioning system (GPS) sensor (108) mounted on the vehicle (10), a location of the vehicle (10);
receiving (408), by a control unit (102) mounted on the vehicle (10), signals indicative of the absolute speed, the acceleration and the location of the vehicle (10);
processing (410), by the control unit (102), the signals indicative of the absolute speed and the acceleration to generate at least a first set of pre-defined parameters, the first set of pre-defined parameters based on time domain information and frequency domain information of the signals indicative of the absolute speed and the acceleration;
determining (412), by the control unit (102), a surface profile index for the detected location based on at least the first set of pre-defined parameters;
comparing (414), by the control unit (102), the determined surface profile index with one or more pre-defined surface profile indices to classify the surface into one of a plurality of pre-defined levels; and
transmitting (416), by the control unit (102), information indicative of the classified level of the surface to one or more devices (112).

15. The method (400) as claimed in claim 14, wherein the first set of pre-defined parameters comprises Continuous Wavelet Transform of the acceleration and Continuous Wavelet Transform of the absolute speed.

16. The method (400) as claimed in claim 14 comprising generating, by the control unit (102), a second set of pre-defined parameters, the second set of pre-defined parameters generated to determine the surface profile index.

17. The method (400) as claimed in claim 16, wherein the second set of pre-defined parameters comprises at least one of: average vehicle speed, mean and power of the signals indicative of the acceleration of the vehicle, occupied bandwidth of acceleration and upper frequency limit of the signals indicative of the acceleration.

18. The method (400) as claimed in claim 14, wherein the plurality of pre-defined levels indicates conditions of the surface, the plurality of predefined levels comprises a first level for well-maintained surfaces, a second level for damaged surfaces and a third level for severely damaged surfaces.

19. The method (400) as claimed in claim 14, wherein the one or more devices (112) comprises at least one of: a display unit (114) of the vehicle (10), a personal digital assistant (116) of the rider, one or more remote servers (118) and an IoT device (120).

20. The method (400) as claimed in claim 17, wherein the control unit (102) comprises a data acquisition unit (122), a prediction unit (124) and a logging unit (126).

21. The method (400) as claimed in claim 20 comprising:
initializing, by the data acquisition unit (122), the one or more speed sensors (104), the one or more accelerometers (106) and the GPS sensor (108);
receiving, by the data acquisition unit (122), the signals indicative of the absolute speed, the acceleration, and the location of the vehicle (10); and
transmitting, by the data acquisition unit (122), the received signals to the prediction unit (124) and the logging unit (126).

22. The method (400) as claimed in claim 21 comprising:
receiving, by the prediction unit (124), the signals from the data acquisition unit (122);
processing, by a signal processor unit (128) of the prediction unit (124), the signals indicative of the absolute speed and the acceleration to generate the first and second sets of pre-defined parameters;
receiving, by a profile index unit (130) of the prediction unit (124), the first and second sets of pre-defined parameters from the signal processor unit (128);
determining, by the profile index unit (130), the surface profile index for the detected location based on the first and second sets of pre-defined parameters;
receiving and comparing, by a profile estimation unit (132) of the prediction unit (124), the determined surface profile index with the one or more pre-defined surface profile indices to classify the surface as one of the plurality of pre-defined levels; and
transmitting, by the profile estimation unit (132), the information indicative of the classified level of the surface to the logging unit (126).

23. The method (400) as claimed in claim 22 comprising:
receiving and storing, by the logging unit (126), the information indicative of the classified level of the surface from the prediction unit (124);
receiving and storing, by the logging unit (126), the signals indicative of the absolute speed, the acceleration, and the location of the vehicle from the data acquisition unit (122); and
transmitting, by the logging unit (126), the stored information and the stored signals to the one or more devices (112), wherein the one or more devices (112) comprises at least one of: a display unit (114) of the vehicle (10), a personal digital assistant (116) of the rider, one or more remote servers (118) and an IoT device (120).

24. The method (400) as claimed in claim 19 comprising generating and transmitting, by the control unit (102), a warning signal to the one or more devices (112) on detection of the classified level of the surface being the second level and the third level.

25. The method (400) as claimed in claim 19 comprising suggesting, by the control unit (102), an alternative route having a level less than the classified level of the surface, the classified level being the second level and the third level.

26. The method (400) as claimed in claim 23 comprising controlling, by the control unit (102), at least one of a suspension (20), a throttle (30), and a brake (40) of the vehicle (10) based on the information indicative of the classified level of the surface.