Apparatus and warning system for intelligent helmets

Abstract

To provide a system for providing information concerning use of a helmet to a rider in a saddle-riding type vehicle such as a motor cycle.SOLUTION: A camera is mounted on a saddle riding-type vehicle and faces a rider, and monitors the rider on the vehicle and collects image data about the rider. A GPS system is configured to detect a position of the saddle riding-type vehicle. A controller communicates with the camera and the GPS system. The controller is configured to receive an image of the rider from the camera and determines whether the rider is wearing a helmet or not, on the basis of the image data about the rider and to output to the rider an indicator for wearing a helmet that is changed based on the detected position of the saddle riding-type vehicle.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an intelligent helmet such as that used while riding a two-wheeled vehicle, such as a motorcycle or dirt bike, a three-wheeled vehicle, or a four-wheeled vehicle, such as an all-terrain vehicle. [Background technology]

[0002] Continuous rider monitoring of a powered two-wheeled vehicle (PTW), such as a motorcycle, can be challenging due to resource constraints, packaging constraints, and other limitations of the PTW. Due to these constraints, as well as environmental and design differences, standard automobile / vehicle safety systems may not be transferable to the PTW.

[0003] The PTW may include an Advanced Rider Assistance System (ARAS) that assists with various functions such as adaptive cruise control and blind spot detection, and the ARAS system may be utilized primarily to generate data for the PTW and provide warning indicators to the rider to improve safety for the rider. Summary of the Invention [Means for solving the problem]

[0004] overview According to one embodiment, a system for providing helmet use information to a rider of a saddle riding vehicle is provided, the system comprising a camera mounted on the saddle riding vehicle facing the rider and configured to monitor the rider of the vehicle and collect image data of the rider, a GPS system configured to detect a position of the saddle riding vehicle, and a controller in communication with the camera and the GPS system, the controller configured to receive images of the rider from the camera, determine based on the rider image data whether the rider is wearing a helmet, receive the position of the saddle riding vehicle from the GPS system, and output a helmet wearing indicator to the rider that is changed based on whether the rider is wearing a helmet and the position of the saddle riding vehicle.

[0005] In another embodiment, the saddle riding vehicle includes a rider-facing camera configured to monitor a rider of the saddle riding vehicle and collect image data of the rider, a GPS system configured to detect a location of the saddle riding vehicle, a human machine interface (HMI) configured to display vehicle information to the rider, and a controller in communication with a helmet safety database containing information regarding penalties for not wearing a helmet in various locations, the controller being configured to identify that the rider is not wearing a helmet based on the rider image data and to output a warning on the HMI in response to the rider not wearing a helmet that changes based on the location of the saddle riding vehicle.

[0006] In another embodiment, a non-transitory computer readable medium stores instructions that, when executed by a computer processor, cause the computer processor to: receive stress data from a stress sensor indicative of stress applied to the saddle riding vehicle seat; receive lidar image data from a lidar-facing camera configured to monitor a rider of the saddle riding vehicle; determine that a rider is seated on the seat in response to the stress exceeding a threshold; compare the lidar image data to a pre-trained model in response to determining that a rider is seated on the seat; and output a helmet wearing indicator to a human machine interface (HMI) that is altered based on a comparison of the lidar image data to the pre-trained model. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 illustrates an example of a system design for a saddle-riding vehicle, such as a motorcycle. [Diagram 2] FIG. 2 is a block diagram of a system 200 for providing a helmet-free warning to, for example, a saddle-rider vehicle operator. [Diagram 3] FIG. 2 illustrates an embodiment of a flowchart of LIDAR detector operations performed by a controller. [Figure 4] FIG. 13 illustrates an embodiment of a flowchart of a helmet detection operation performed by the controller. [Diagram 5] FIG. 1 illustrates one embodiment of a flowchart of safety verification operations performed by a controller. [Figure 6] FIG. 13 illustrates an embodiment of a flow chart for a two-stage indicator executed by a controller to provide various warnings to a driver. [Figure 7A] FIG. 13 illustrates an example of an intensity indicator provided to a driver, for example, when the driver or rider's helmet is not being worn. [Figure 7B]FIG. 13 illustrates an example of a mild indicator provided to a driver or rider, for example, if the driver or rider's helmet is worn but not worn properly, or if the driver or rider is not wearing a helmet in a location where such behavior is not illegal. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Detailed Description The embodiments of the present disclosure are described herein. However, it should be understood that the disclosed embodiments are merely exemplary and that other embodiments may take various alternative forms. The drawings are not necessarily to scale. Some features may be exaggerated or minimized to show the details of a particular component. Therefore, specific structural and functional details disclosed herein should not be interpreted as limiting, but rather as merely representative examples for teaching those skilled in the art how to use various embodiments. As will be understood by those skilled in the art, various features shown and described with reference to any one of the drawings can be combined with features shown in one or more other drawings to produce embodiments not explicitly shown or described. The combination of the illustrated features provides a representative embodiment for a typical application. However, various combinations and modifications of features consistent with the teachings of the present disclosure may be required for a particular application or implementation.

[0009] This disclosure refers to helmets and saddle-ridden vehicles. This "saddle-ridden vehicle" typically refers to a motorcycle, but it should be understood that it can also include any type of motor vehicle in which the driver typically sits in a saddle and a helmet is worn since there is typically no cabin for rider protection. It also includes other motorized two-wheel vehicles (PTWs) such as dirt bikes or scooters in addition to motorcycles. It also includes motorized three-wheel vehicles or four-wheel vehicles such as all-terrain vehicles (ATVs). In particular, any reference to a motorcycle is also applicable to any other saddle-ridden vehicle unless otherwise specified.

[0010] Helmets are a preventative measure that improves safety on motorcycles, and seat belts are a preventative measure that improves safety in other vehicles. According to the National Highway Traffic Safety Administration (NHTSA), helmets are 37% effective in preventing fatal injuries to motorcycle riders and 41% effective for motorcycle passengers (backseat riders). However, according to NHTSA, in 2018, only 71% of motorcycle riders used helmets, and in 2016, nearly 41% of motorcycle accident fatalities involved riders who were not wearing helmets. Due to the safety that helmets provide to riders, many states and countries require helmets for all drivers and all backseat riders. Nevertheless, riders sometimes avoid helmets and do not comply with safety laws.

[0011] Existing approaches to monitoring helmet use include the use of roadside cameras to monitor traffic and identify whether a helmet is being worn. Other helmet warning systems include the use of sensors mounted on the helmet to identify whether a helmet is being worn. However, having additional sensors on the helmet increases the cost of the helmet and is not possible to use with existing helmets available on the market.

[0012] Therefore, according to the embodiments disclosed herein, a system is disclosed for providing a driver of a saddle riding vehicle with status information regarding helmet use. The system can provide timely warnings directly to the driver to ensure that the driver and pillion rider are wearing their helmets properly. The system includes one or more sensors and a processing unit mounted on the saddle riding vehicle itself without any modification to the existing helmet. In other words, the system can work with existing helmets on the market since there is no need to fit or mount the sensor or processing unit on the helmet itself. Using the information provided by the system, the rider can be warned that the driver or rider is not wearing a helmet, for example, using a visual, audio or tactile indicator.

[0013] FIG. 1 is an example of a system design 100 for a saddle riding vehicle 102, such as a motorcycle. The saddle riding vehicle 102 may include one or more lidar cameras 104. Each lidar camera 104 may face forward and rearward of the saddle riding vehicle so that each lidar camera 104 can see its respective lidar. For example, one lidar camera may be provided facing the driver and another lidar camera may be provided facing a pillion rider behind the driver. In other embodiments, one lidar camera may be positioned at an angle (e.g., offset) so that it can see both the driver and pillion rider. The lidar cameras 104 may be used to capture images or videos of the lidar, which are then used for various calculations, such as identifying different body parts or movements of the rider.

[0014] The saddle riding vehicle 102 may include an inertial measurement unit (IMU) 106. The IMU 106 may be mounted or otherwise attached to a front portion of the saddle riding vehicle 102, such as a headlight or other similar area. The IMU 106 may collect inertial data that may be used to understand the motion of the saddle riding vehicle 102. For example, the IMU 106 may be or include a multi-axis accelerometer, such as a 3-axis accelerometer, a 4-axis accelerometer, a 5-axis accelerometer, a 6-axis accelerometer, etc. The IMU 106 may operate in conjunction with a processor or controller (described below) to determine the position and orientation of the saddle riding vehicle relative to a reference point.

[0015] The saddle riding vehicle 102 may include one or more seat sensors 108. The driver's seat may include one or more seat sensors, and may also include a rear seat (if such a seat is provided). The seat sensor 108 may include a stress sensor located within the seat itself to output a signal to a processor or controller (described below) regarding the stress distributed on the seat. If the stress exceeds a threshold, the processor or controller may infer that a person is seated on the seat. In other embodiments, a camera (such as camera 104) may be the seat sensor, in which case the processor or controller may receive one or more images from the camera and compare the images to various databases. For example, one database may provide an image of the seat as it appears when the driver is not on the seat, and another database may provide an image of the seat as it appears when the driver is on the seat. The processor or controller may receive the image, compare the image to the database, and detect whether the driver is seated on the seat based on the comparison. The databases may be pre-populated by the manufacturer of the saddle riding vehicle. The seat sensor 108 may include a combination of a stress sensor and a camera. In one embodiment, the processor or controller determines that a person is present on the seat when both the stress sensor and the camera provide such an indication.

[0016] The saddle riding vehicle 102 also includes an electronic control unit (ECU) 110. The ECU may be more generally referred to as a controller, which may be any controller capable of receiving information from various devices (e.g., lidar camera, IMU, and seat sensor, etc.), processing the information, and outputting instructions to a human machine interface or haptic feedback system. In this disclosure, the terms "controller" and "system" may refer to, be part of, or include processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores the code executed by the processor hardware. The code is configured to provide the functionality of the controller and system described herein. In one example, the controller may include a processor, memory, and non-volatile storage. A processor may include one or more devices selected from a microprocessor, a microcontroller, a digital signal processor, a microcomputer, a central processing unit, a field programmable gate array, a programmable logic device, a state machine, a logic circuit, an analog circuit, a digital circuit, or any other device that manipulates signals (analog or digital) based on computer-executable instructions resident in memory. Memory may include a single memory device or multiple memory devices, including but not limited to random access memory ("RAM"), volatile memory, non-volatile memory, static random access memory ("SRAM"), dynamic random access memory ("DRAM"), flash memory, cache memory, or any other device that may store information. Non-volatile storage may include one or more permanent data storage devices, such as hard drives, optical drives, tape drives, non-volatile solid-state devices, or any other device that may permanently store information.The processor may be configured to execute computer-executable instructions that are loaded into the memory and implement one or more software programs that are resident in the non-volatile storage. The programs that are resident in the non-volatile storage may include or be part of an operating system or application, and may further be compiled or interpreted from computer programs written using a variety of programming languages ​​and / or technologies, including, but not limited to, Java, C, C++, C#, Objective C, Fortran, Pascal, Java Script, Python, Perl, and PL / SQL, alone or in combination. The computer-executable program instructions, when executed by the processor, may be configured to generate visual, auditory, or haptic feedback that is provided to the rider based on determining that the rider's helmet is not worn or is not adequately secured, as described more fully herein.

[0017] Implementations and operations of the subject matter described herein can be implemented in digital electronic circuitry, or in computer software embodied on tangible media, firmware, or hardware including the structures disclosed herein and their structural equivalents, or in any combination or combinations thereof. Implementations of the subject matter described herein can be implemented as one or more computer programs embodied on tangible media, i.e., as one or more modules of computer program instructions encoded on one or more computer storage media for execution by or to control the operation of a data processing apparatus. A computer storage medium can be or be included in a computer readable storage device, computer readable storage substrate, random or serial access memory array or device, or any combination or combinations thereof. A computer storage medium can also be or be included in one or more separate components or media (e.g., multiple CDs, disks, or other storage devices). A computer storage medium may be tangible and non-transitory.

[0018] A computer program (also known as a program, software, software application, script, or code) can be written in any type of programming language, including compiled, interpreted, declarative, and procedural languages, and furthermore, a computer program can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program can, but does not necessarily, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), a single file dedicated to a query program, or multiple coordinated files (e.g., a file that stores one or more modules, libraries, subprograms, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0019] The processes and logic flows described herein may be performed by one or more programmable processors that execute one or more computer programs to perform actions by manipulating input data and generating output. The processes and logic flows may also be performed by or implemented as special purpose logic circuitry, such as a field programmable gate array ("FPGA") or an application specific integrated circuit ("ASIC"). Such special purpose circuitry may be referred to as a computer processor even if it is not a general purpose processor.

[0020] The saddle riding vehicle 102 also includes various outputs to warn the operator that one or more riders' helmets are not being worn, are not being worn properly, are unsafe, etc. The warning outputs may be provided to a human machine interface (HMI) 112 and / or a haptic feedback system 114.

[0021] The HMI 112 provides a user interface that connects the driver of the saddle-riding vehicle 102 to a controller such as an ECU. The HMI 112 may be in the form of an instrument cluster or screen. The screen may be a touch screen, including, for example, touch-sensitive resistive / capacitive buttons. The HMI 112 may include or be part of a dash that provides the driver with vehicle information such as vehicle speed, engine speed, fuel availability, fuel economy, and the like. As described herein, the HMI 112 may also provide the driver with information regarding the status of the driver's or other rider's helmet. For example, the HMI 112 may provide the driver with a visual warning that his or her helmet is not being worn. Examples of such visual warnings are shown in FIGS. 7A-7B and described below.

[0022] In addition to (or as an alternative to) the HMI, the saddle-riding vehicle 102 may also provide a helmet warning to the driver via a haptic feedback system 114. The haptic feedback system 114 may include, for example, a vibrator configured to vibrate in response to determining that the driver or rider is not wearing his or her helmet. The vibrator may be provided at various locations on the saddle-riding vehicle 102, such as on the handlebars, the seat, the pedals, etc. The haptic feedback at these locations alerts the driver that there is a message to be delivered to the driver. The driver may then look at the HMI to see what visual notification is provided, such as a no-helmet warning.

[0023] FIG. 2 shows a block diagram of a system 200 for providing helmet-off warning to a driver of a saddle-riding vehicle. The system 200 includes various inputs, such as vehicle sensors 202 and a helmet database 204. The vehicle sensors 202 may include, for example, the lidar camera 104, the IMU 106, and the seat sensor 108. The helmet database 204 may be preprogrammed or stored in a memory associated with the controller. The helmet database 204 may include multiple stored images of different helmets. For example, many (if not all) helmet images available on the market may be provided to the controller. These images may be from different angles showing all sides of each helmet.

[0024] Various algorithms are executed by the controller of the saddle riding vehicle 102. For example, based on the vehicle sensors 202, the controller can execute a lidar detector operation 206 and a helmet detector operation 208. FIG. 3 illustrates an embodiment of the lidar detector operation 206, while FIG. 4 illustrates an embodiment of the helmet detector operation 208. Based on the helmet database 204 and the output of the lidar detector operation 206 and the helmet detector operation 208, the controller can execute a safety verifier operation 210. Based on the output of the safety verifier operation, the controller can provide an HMI feedback operation 212, which can output a message to the instrument cluster 214 to alert the driver that his or her helmet or the helmet of another rider is not being worn. FIG. 5 illustrates an embodiment of the safety verifier operation 210, and FIG. 6 illustrates an embodiment of the HMI feedback operation 212.

[0025] Referring to FIG. 3, one embodiment of the lidar detector operation 206 is shown. The lidar detector operation 206 may be performed by the controller based on input from the vehicle sensor 202. The operation begins at step 300. At step 302, the controller determines if the saddle riding vehicle has been started. This may be done by examining the ignition, data signals sent from the key insert, engine fueling data, and other methods. This may also be done by looking at electrical signal activity transmitted by the vehicle's data bus, along with the amount of power transferred to the system from the on-board battery to prepare the system for ignition. If the saddle riding vehicle has not been started, the method ends at step 304 and returns to continually checking if the vehicle has been started. This continuous checking may be done at short intervals to avoid wasting battery power.

[0026] If the saddle riding vehicle is started, then the controller receives a seat stress signal from the seat stress sensor in step 306. This signal can indicate how much weight or stress is distributed on the seat. In one embodiment, the weight sensor is located directly under the seat bottom. In another embodiment, the seat stress signal can be generated from a sensor system inside the seat. This sensor system includes a pressure sensor, a silicone filled blast or container, and an associated ECU or controller as described above. When a person sits on the seat, the pressure sensor informs the controller of the weight of the occupant.

[0027] In step 308, the controller compares the seat stress or weight to a threshold value. For example, the threshold value may be set to 100 pounds of weight or equivalent stress. If the detected stress exceeds this threshold value, the controller determines that a rider is seated on the seat in step 310. The method then ends in step 312.

[0028] However, if the seat stress does not exceed the threshold in step 308, then the controller can perform a different process to detect whether a rider may still be seated on the seat. For example, in step 314, the controller receives images of the lidar from a camera facing the lidar. In step 316, the controller compares these images from the camera to a pre-trained model. This pre-trained model may be a database of images that appear to show a person seated on the seat, and other images that appear to show a seat without a person seated on it. In step 318, the controller can determine whether there is a sufficient match between the image received from the camera and a stored image of a person seated on the seat. If there is a sufficient match, the controller identifies that a rider is seated on the seat. If there is no such sufficient match, then the controller identifies that no rider is seated on the seat in step 320.

[0029] Referring to FIG. 4, one embodiment of the helmet detector operation 208 is shown. The operation begins at step 400. At step 402, the controller identifies whether a lidar is detected using the operation shown in FIG. 3. If a lidar is detected from step 310, then the controller receives a camera image at step 404. This can be a similar step to step 314, where a camera facing the lidar captures one or more images of the lidar. In other embodiments, one or more 3D sensors other than a camera (e.g., a LIDAR-based sensor) are utilized to detect the contour and shape of the rider's head and / or helmet. At step 406, the controller compares the image received from the camera to a pre-trained model. This pre-trained model may be a database of various helmets, including multiple makes and models of helmets available on the market. The pre-trained model may include images of the helmet from multiple angles. The pre-trained model may also include a 360-degree view of the helmet around one or more axes, providing a nearly complete image of each helmet in the database. A database of these images can be stored in a memory connected to the controller. The pre-trained model can be generalized and improved by simulating different background environments. Contour methods can be used to extract the edges of the helmet, which are used for updating the reference helmet model. Input from multiple image captures from the camera as the rider moves can be used for updating the helmet database. These images can first be stabilized and intensity corrected before being used for prediction. If a helmet is detected, a 2D / 3D model of the helmet can be estimated.

[0030] In some embodiments, the helmet detector operation may end at step 406. Comparison of the image received from the camera and updating of the image stored in memory can be used for other algorithms and systems, such as a separate display system or a safety verifier as illustrated in Figure 5. In this embodiment shown in Figure 4, the system proceeds to step 408, where the controller determines whether there is a sufficient match between the image received from the camera and the image in the pre-trained model. If there is not a sufficient match, then the method can return to step 402 to determine whether a lidar has been detected.

[0031] If there is a sufficient match, the method proceeds to step 410, where the controller identifies whether there is a match of helmet wearing. In this step, the controller compares images of the person wearing the helmet stored in a database with the images captured from the camera. Similar to step 406, the controller may communicate with a pre-trained model or database of images of people wearing a particular helmet detected by the camera in step 408. Multiple angles of images of a particular make / model of helmet on a person may be taken and multiple images of the person wearing the helmet may be stored in memory. If there is a sufficient match, the controller may consider the helmet to be properly worn by the rider and the method may return to step 402.

[0032] However, if there are not enough helmet-wearing matches or if there is a match between the image of the person not wearing the helmet properly and the image captured by the camera, then the controller considers the helmet not to be properly worn in step 412. The helmet not-wearing indicator can be commanded by the controller. This indicator can be a visual, auditory or haptic feedback to the rider. For example, the controller can command the HMI to display a warning that the rider's helmet is not worn properly. If the rider's helmet is equipped with one-way or two-way audio capabilities that allow the helmet to communicate with the motorcycle, or has a mobile device that communicates with the vehicle, then the controller can access this system to send an auditory warning to the rider that the helmet is not worn or not worn properly. The controller can also provide haptic feedback to the rider indicating that the helmet is not worn properly. For example, the controller can cause vibrations in the rider's seat by outputting a command to activate a seat vibrator. In other embodiments, the controller can cause vibrations in the handlebars. In other embodiments, the controller may issue a command to the vehicle's brakes to apply a small amount of braking force to the wheels for a very short period of time. Any of the above example haptic feedback systems may be utilized to attempt to get the driver or rider to look at the HMI for a message indicating that their helmet is not being worn or is not being worn properly.

[0033] The method illustrated in Figure 4 may be executed continuously during a driving event. For example, the algorithm may be executed once the controller detects that the driver is seated in the seat, and it may be executed continuously until the vehicle is deactivated. In another embodiment, the algorithm is initiated in response to a key-on event that starts the vehicle.

[0034] Referring to FIG. 5, one embodiment of the safety verifier operation is shown. In this embodiment, the controller is configured to provide a multi-level (e.g., two-level) helmet-not-wearing warning to the rider depending on the position of the saddle-riding vehicle 102. The process begins at step 500. At step 502, the controller receives one or more images from an on-board camera, similar to steps 404 and 314 described above. At step 504, the controller compares the received camera image to a database of helmet model images or a pre-trained model, similar to step 406, for example. Again, at this step, the controller may analyze, for example, the contours and edges of the helmet and the rider. At step 506, the controller determines whether there is a sufficient match between the received camera image and the image database. This may be similar to step 408, for example.

[0035] In one embodiment, steps 502 through 506 may result in a determination of whether the rider is wearing his or her helmet and whether it is worn properly, which may include all steps from the algorithm shown in Figure 4. In other words, the safety verifier operation of Figure 5 begins by first identifying whether the rider and passenger helmets are worn and whether they are worn properly according to the method disclosed in Figure 4.

[0036] After determining whether the helmet is being worn and whether it is being worn properly, the method proceeds to step 508, where the controller receives Global Positioning System (GPS) data of the vehicle's location. The controller can, for example, access an on-board GPS system (e.g., a transceiver and associated GPS controller configured to communicate with satellites) on the vehicle performing navigation to receive information (e.g., coordinates) of the vehicle. GPS is one example of determining the vehicle's location, and in other embodiments, the controller accesses location data from the Global Navigation Satellite System (GLONASS), etc. In other embodiments, the controller accesses the vehicle's location by communicating with a mobile device (e.g., a mobile phone, smart watch, other wearable device, etc.) connected to the vehicle while driving. For example, the mobile device may be connected to a receiver (e.g., a Bluetooth® Low Energy (BLE) receiver, Near Field Communication, Ultraband, Zigbee®, etc.) on the vehicle that is used to share data while traveling, make calls, unlock the vehicle, start the vehicle, etc. Once that communication is established, the saddle riding vehicle 102 can receive location information from the mobile device carried by the driver.

[0037] In step 510, the controller can access a database of legal penalties for not wearing a helmet based on the vehicle location information. The database of legal penalties for each location may include a database of penalties by state or county in the United States, or a database of penalties by country. For example, the state of Michigan may have no legal penalties for not wearing a helmet, while the state of New York may have legal penalties for not wearing a helmet (e.g., a fine of up to $100, 30 days in jail, or both). The database of legal penalties may be preprogrammed into an on-board memory with access by the controller. Alternatively, the database may be located on the driver's mobile device. In either embodiment, the database may be continually updated to include current laws.

[0038] In step 512, the controller identifies whether the driver and / or rider is wearing his / her helmet and whether it is worn properly. This may be, for example, a similar identification as that performed by the algorithm of FIG. 4. If the system reaches a determination that the helmet is not worn or is not worn properly, then it should provide a helmet not worn indicator (e.g., step 412) to the driver, for example, via the HMI. This helmet not worn indicator may include information regarding legal penalties associated with the vehicle's current location for driving or riding without a helmet being worn properly. Other information may be provided on the HMI based on the current helmet usage and vehicle location. For example, the controller may cause the appearance of visual text that warns the driver that their helmet is not worn properly and / or that it is illegal to continue riding without a helmet being worn properly. Details of the penalty (e.g., monetary or imprisonment) may be included in the warning provided on the HMI. In other embodiments, the warning is provided to the audio system on the driver's helmet speakers, as described above.

[0039] In one embodiment, a two-stage indicator is utilized. An example of this is shown in FIG. 6. The algorithm begins at step 600. At step 602, the controller identifies whether a helmet is being worn and whether it is being worn properly, similar to step 506 or step 410 above. If the helmet is not being worn, then at step 604, an indicator of strength is provided to the driver. This can be done, for example, by displaying a first helmet-not-wearing indicator on the HMI, an example of which is shown in FIG. 7A, which shows an image of a helmet surrounded by a red cross. This indicates that it is illegal to move the vehicle without a properly worn helmet at the current location. If the helmet is being worn, then a determination is made at step 606 whether the worn helmet meets safety requirements. This determination can be made, for example, by comparing the image from the camera to a database of properly worn helmets. The helmet may be worn by the driver but not worn properly (e.g., worn too high or too low, or the helmet's identification indicates that the helmet is old and no longer safe to wear, etc.). In other embodiments, if the wearing of the helmet does not meet the safety requirements, then in step 608, a mild indicator is provided to the driver. This can be done, for example, by displaying a second helmet not worn indicator on the HMI, an example of which is shown in FIG. 7B, which shows an image of a helmet surrounded by a yellow cross. This indicates that the helmet may be worn but may nevertheless not meet the current safety requirements. Colors other than red and yellow may also be utilized. If the helmet meets the safety requirements, then in step 610, no indicator is provided.

[0040] In other embodiments, the determination of whether wearing a helmet violates the laws of the current jurisdiction may result in a decision to provide a mild indicator in step 608 or no indicator in step 610. For example, even if a positive determination is made in step 602 that the driver or rider is wearing his or her helmet, an additional determination may be made regarding the current laws of the vehicle's location. This determination may be made from steps 510 through 514 described above. If the laws of the current jurisdiction allow the driver or rider to ride a saddle riding vehicle without penalty, a mild indicator may be provided to the driver even if the driver is wearing his or her helmet.

[0041] The processes, methods or algorithms disclosed herein can be delivered / implemented in a processing device, controller or computer, which may include any existing programmable or dedicated electronic control unit. Similarly, these processes, methods or algorithms can be stored as data and instructions executable by a controller or computer in many forms, including but not limited to information permanently stored in a non-writable storage medium, such as a ROM device, and information mutably stored in a writable storage medium, such as a floppy disk, magnetic tape, CD, RAM device, other magnetic and optical medium. These processes, methods or algorithms can also be implemented in software executable objects. Alternatively, these processes, methods or algorithms can be implemented in whole or in part using suitable hardware components, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers, or other hardware components or devices, or a combination of hardware, software and firmware components.

[0042] Although exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The terms used herein are terms of description rather than limitation, and it is to be understood that various modifications may be made without departing from the spirit and scope of the present disclosure. As previously described, features of various embodiments may be combined to form further embodiments of the present invention that may not be explicitly described or illustrated. Although various embodiments may be described as offering advantages or being preferred over other embodiments or aspects of the prior art in terms of one or more desired characteristics, those skilled in the art will recognize that one or more features or characteristics may be traded off to achieve desired overall system attributes that depend on the particular application and implementation. These attributes may include, but are not limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, maintainability, weight, manufacturability, ease of assembly, and the like. Thus, to the extent that any embodiments are described as less desirable than other or conventional embodiments with respect to one or more characteristics, those embodiments do not depart from the scope of the present disclosure and may still be desirable for particular applications.

Claims

1. 1. A system for providing helmet use information to a rider of a saddle riding vehicle, comprising: The system comprises: a camera mounted to the saddle riding vehicle facing the rider and configured to monitor the vehicle's rider and collect image data of the rider; a GPS system configured to detect a location of the saddle riding vehicle; a controller in communication with the camera and the GPS system; Equipped with The controller: receiving an image of the lidar from the camera; Identifying whether the rider is wearing a helmet based on the image data of the rider; receiving a location of the saddle riding vehicle from the GPS system; outputting a helmet wearing indicator to the rider that changes based on whether the rider is wearing a helmet and the position of the saddle riding type vehicle; It is configured as follows: system.

2. The controller is configured to communicate with a database to receive information regarding legality of helmet use at the location of the saddle riding vehicle from the GPS system, and to modify the helmet wearing indicator based on the legality of helmet use at the received location. The system of claim 1 .

3. The controller is configured to output the helmet wearing indicator on a human machine interface (HMI) display on the saddle riding vehicle. The system of claim 2.

4. The controller is configured to output a mild helmet wearing indicator on the HMI in response to determining that the rider is not wearing a helmet and that the saddle riding vehicle is in a location where riding the saddle riding vehicle without a helmet is legal. The system of claim 3.

5. The controller is configured to output an intense helmet wearing indicator on the HMI in response to determining that the rider is not wearing a helmet and that the saddle riding vehicle is in a location where riding the saddle riding vehicle without a helmet is illegal. The system of claim 4.

6. The controller is configured to output the helmet-wearing indicator via a haptic feedback system. The system of claim 2.

7. The system further includes a vibrator configured to be mounted to a seat of the saddle riding vehicle, the controller configured to activate the vibrator in response to determining that the rider is not wearing a helmet and that the saddle riding vehicle is located in a location where riding the saddle riding vehicle without a helmet is illegal. The system of claim 6.

8. A saddle riding type vehicle, a lidar-facing camera configured to monitor a lidar of the saddle riding vehicle and collect image data of the lidar; a GPS system configured to detect a location of the saddle riding vehicle; a human machine interface (HMI) configured to display vehicle information to the lidar; a controller in communication with a helmet safety database containing information regarding penalties for not wearing a helmet in various locations; Equipped with The controller: Identifying that the rider is not wearing a helmet based on image data of the rider; In response to the rider not wearing a helmet, a warning is output on the HMI, the warning being changed based on the position of the saddle riding type vehicle. It is configured as follows: Saddle riding vehicle.

9. The controller is configured to output a first warning to the HMI in response to a position of the saddle riding vehicle corresponding to a first location where there is no penalty for not wearing a helmet.

9. The saddle riding type vehicle according to claim 8.

10. The first warning is displayed in a first color.

10. The saddle riding type vehicle according to claim 9.

11. The controller is configured to output a second warning to the HMI in response to a position of the saddle riding vehicle corresponding to a second location where there is a penalty for not wearing a helmet. The saddle riding type vehicle according to claim 10.

12. The second warning is displayed in a second color different from the first color.

12. The saddle riding type vehicle according to claim 11.

13. the second warning includes information regarding penalties for not wearing a helmet, 12. The saddle riding type vehicle according to claim 11.

14. The controller is configured to identify that the rider is improperly wearing a helmet based on image data of the rider.

12. The saddle riding type vehicle according to claim 11.

15. The controller is configured to output the second warning to the HMI in response to the helmet being improperly worn.

15. The saddle riding type vehicle according to claim 14.

16. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by a computer processor, causing the computer processor to: receiving stress data from a stress sensor in communication with the saddle riding vehicle seat and indicative of stress applied to the saddle riding vehicle seat; receiving lidar image data from a lidar facing camera configured to monitor a rider of the saddle riding vehicle; determining that the rider is seated on the seat in response to the stress exceeding a threshold; responsive to identifying the rider being seated on the seat, comparing image data of the rider to a pre-trained model; determining whether the rider is wearing a helmet based on a comparison of the image data of the rider to the pre-trained model; and outputting a helmet wearing indicator to a human machine interface (HMI) that is altered based on determining that the rider is wearing a helmet; Execute the Non-transitory computer-readable medium.

17. The instructions further cause the computer processor to receive a location of the saddle riding vehicle from a GPS system and modify the helmet wearing indicator based on the location of the saddle riding vehicle. The medium according to claim 16.

18. The instructions further cause the computer processor to obtain information regarding penalties for not wearing a helmet at the location of the saddle riding vehicle. The medium according to claim 17.

19. the helmet wearing indicator includes a first indicator of a first intensity based on the vehicle being in a first location where riding a vehicle without a helmet is legal, and a second indicator of a second intensity based on the vehicle being in a second location where riding a vehicle without a helmet is illegal. The medium of claim 18.

20. the first indicator includes a first image displayed on the HMI in a first color, and the second indicator includes a second image displayed on the HMI in a second color different from the first color. The medium of claim 19.

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