Apparatus and system relating to intelligent helmet

Abstract

To provide a device and a system related to an intelligent helmet.SOLUTION: A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet also includes an inertial movement unit (IMU) configured to collect helmet motion data of a rider of the vehicle and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle and determine a rider attention state utilizing the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an intelligent helmet such as may be utilized on a motorcycle or dirt bike, three-wheeled vehicle, or other vehicle, including four-wheeled vehicles such as all-terrain vehicles. [Background technology]

[0002] 2. Background of the Invention Continuous monitoring of motorcycle riders can be challenging due to resource constraints and limitations of powered two-wheeled vehicles (PTWs). In contrast to automotive safety systems, motorcycles may have non-transferable technology due to different constraints of PTWs due to environmental and design differences.

[0003] The motorcycle may include an Advanced Rider Assistance System (ARAS) to assist with various functions such as adaptive cruise control, blind spot detection, etc. The ARAS system may be utilized primarily for data generation by the vehicle. The system may provide warning indicators to enable rider safety on the PTW. Summary of the Invention [Means for solving the problem]

[0004] Summary of the Invention According to one embodiment, the helmet includes a transceiver configured to receive vehicle data from one or more sensors disposed on the vehicle. The helmet also includes an inertial measurement unit (IMU) configured to collect helmet motion data of a rider of the vehicle, and a processor in communication with the transceiver and the IMU. The processor is further programmed to receive the vehicle data from the one or more sensors disposed on the vehicle via the transceiver, and to identify an attention state of the rider using the vehicle data from the one or more sensors disposed on the vehicle and the helmet motion data from the IMU.

[0005] According to a second embodiment, a system includes a helmet and a vehicle having at least two wheels. The system includes a helmet transceiver configured to receive vehicle data from one or more sensors disposed on the vehicle, and a helmet inertial measurement unit (IMU) configured to collect helmet motion data of a rider of the vehicle. Also, system The system includes a lidar-facing camera disposed on the vehicle and configured to monitor a rider of the vehicle and collect lidar image data. The system also includes a processor in the helmet in communication with the helmet transceiver and the helmet IMU. The processor is programmed to receive vehicle data from one or more sensors disposed on the vehicle via the helmet transceiver and to identify an attention state of the rider using the vehicle data from the one or more sensors disposed on the vehicle and the helmet motion data from the IMU.

[0006] According to a third embodiment, the motorcycle transmits motorcycle data from one or more sensors disposed on the motorcycle to the helmet; helmet The motorcycle also includes a vehicle inertial measurement unit (IMU) configured to collect vehicle motion data for a rider of the motorcycle, and a processor in communication with the transceiver and the vehicle IMU, the processor receiving the helmet data from one or more sensors disposed in the helmet; helmet Using helmet data from one or more sensors placed on the vehicle and vehicle motion data from the vehicle IMU, the rider's attention is adjusted situation is programmed to identify [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is an exemplary diagram of a system design 100 including a smart helmet and a motorcycle. [Diagram 2] 2 is an example of a flowchart 200 for verification of rider attention. [Diagram 3] 3 is an exemplary diagram 300 of a head pose estimator. [Figure 4] 4 is an example diagram 400 of a rider body posture estimator. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Working Example The embodiments of the present disclosure are described herein. However, it is understood that the disclosed embodiments are merely exemplary and that other embodiments may take various alternative forms. The drawings are not necessarily to scale, and some features may be exaggerated or minimized to show details of specific components. Therefore, specific structural and functional details disclosed herein should not be interpreted as limiting, but merely as a representative basis for teaching those skilled in the art various uses of the 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 provide embodiments not explicitly shown or described. The combinations of the illustrated features provide embodiments representative of typical applications. However, various combinations and modifications of features consistent with the teachings of the present disclosure may be desired for specific applications or embodiments.

[0009] Continuous monitoring of the rider's state may be essential for the ARAS to work in conjunction with active safety systems. Rider posture, head position and orientation are essential for active ARAS. Additionally, contact recognition may enable a dynamic rider-bike model. In contrast to automobiles, a helmet may be required for the PTW. Current smart motorcycle helmets can extend the instrument cluster and dashboard to provide important information to a Head-Up Display (HUD). Sensors can be added to the helmet to track the rider's head pose to help identify the human driver's intent or to be used to determine what to display in the HUD. By using visual and inertial sensors in the helmet and PTW, the system may enable continuous monitoring of the rider's attention, posture, contacts, and spatial relationship to the PTW.

[0010] The rider state can be described by the pose (e.g., position and orientation) of the head, upper body joints, and contact parts with respect to the PTW and the outside world. The estimated rider state can be used to identify the rider's attention, posture, slippage, etc. required for the ARAS application. Here, the rider's attention can mean the gaze direction in which the rider's gaze is focused. The rider's posture can mean the position of the rider's back, neck, shoulders, arms, and other upper body parts. The rider's contact can mean the rider's grip of the rider's hands and the handlebars or other parts of the motorcycle. The rider's slippage can mean the contact between the rider's buttocks and the seat of the motorcycle. Other rider state information can include the rider's relationship to the vehicle and the rider's relationship to the outside world.

[0011] FIG. 1 is an example of a system design 100 including a smart helmet 101 and a motorcycle 103. The smart helmet 101 and the motorcycle 103 may include various components and sensors that interact with each other. The smart helmet 101 may focus on collecting data related to the movement of the body and head of the rider. In one embodiment, the smart helmet 101 may include a camera 102. The camera 102 of the helmet 101 may include a first sensor that is used for recognition of the position and orientation of the moving vehicle. Thus, the camera 102 may point outside the helmet 101 and track other vehicles and objects around the rider. The camera 102 may have difficulty capturing the dynamics of such objects and vehicles. In other embodiments, the helmet 101 may include a radar sensor or a LIDAR sensor in addition to or instead of the camera 102.

[0012] The helmet 101 may also include a helmet inertial measurement unit (IMU) 104. The helmet IMU 104 may be used to track highly dynamic movements of the rider's head. Thus, the helmet IMU 104 may be used to track the direction the rider is facing or the direction of the rider's gaze. Additionally, the helmet IMU 104 may be used to track sudden movements and other events that may occur. The IMU may include one or more motion sensors.

[0013] Inertial Measurement Units (IMUs) can use a combination of accelerometers and gyroscopes, and sometimes magnetometers, to measure and report specific forces, angular velocities of the body, and sometimes magnetic fields around the body. IMUs are typically used to pilot aircraft, including unmanned aerial vehicles (UAVs), and especially spacecraft, including satellites and landers. IMUs can be used as components of inertial navigation systems used in a variety of vehicle systems. Data collected from IMU sensors can enable a computer to track the position of the motorcycle.

[0014] An IMU can operate by detecting current acceleration using one or more accelerometers, and one or more gyroscopes can detect changes in rotational attributes such as pitch, roll, and yaw. A typical IMU also includes a magnetometer that can be used to help calibrate against orientation drift. Inertial navigation systems include IMUs with angular and linear accelerometers (for position changes), and some IMUs include a gyroscopic element (for maintaining an absolute angular reference). The angular velocity meter measures how the vehicle is rotating in space. There can be at least one sensor for each of the three axes: pitch (nose up / down), yaw (nose left / right), and roll (clockwise or counterclockwise as viewed from the driver's seat). A linear accelerometer can measure the non-gravitational acceleration of the vehicle. Since motion can occur in three axes (up / down, left / right, front / back), there should be one linear accelerometer per axis. The three gyroscopes are typically arranged in a similar orthogonal pattern to measure rotational position with respect to an appropriately selected coordinate system. A computer can continuously calculate the current position of the vehicle. For each of the six degrees of freedom (x, y, z and Ox, Oy, Oz), the computer can integrate the measured acceleration with respect to time along with an estimate of gravity to calculate the current velocity. It can also integrate the velocity to calculate the current position. Some of the measurements provided by the IMU are:

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[0015] The helmet 101 may include an eye tracker 106. The eye tracker 106 may be used to determine the direction in which the rider of the motorcycle 103 is looking. The eye tracker 106 may be used to identify drowsy and tired or PTW riders. The eye tracker 106 may identify different parts of the eye (e.g., retina, cornea, etc.) to determine where the user is looking. The eye tracker 106 may include a camera or other sensor to assist in tracking the rider's eye movements.

[0016] The helmet 101 may also include a helmet processor 108. The helmet processor 108 may be utilized for sensor fusion of data collected by various cameras and sensors on both the motorcycle 103 and the helmet 101. In other embodiments, the helmet may include one or more transceivers utilized for short-range and long-range communications. The short-range communications of the helmet may include communications with the motorcycle 103 or with other vehicles and objects in the vicinity. In other embodiments, the long-range communications may include communications with off-vehicle servers, the Internet, the "cloud," cellular networks, and the like. The helmet 101 and the motorcycle 103 may communicate with each other utilizing wireless protocols implemented in transceivers located on both the helmet 101 and the motorcycle 103. Such protocols may include Bluetooth, Wi-Fi, and the like. The helmet 101 may also include a head-up display (HUD) utilized to output graphic images to the visor of the helmet 101.

[0017] The motorcycle 103 may include a forward facing camera 105. The forward facing camera 105 may be located in the headlight or other similar area of ​​the motorcycle 103. The forward facing camera 105 may be utilized to assist in identifying where the PTW is facing. Additionally, the forward facing camera 105 may also identify various objects or vehicles in front of the motorcycle 103. Thus, the forward facing camera 105 may assist various safety systems, such as intelligent cruise control or collision detection systems.

[0018] The motorcycle 103 may include a bike IMU 107. The bike IMU 107 may be attached to the headlight or other similar area of ​​the PTW. The bike IMU 107 may collect inertial data that can be used to understand the motion of the bike. The bike IMU 107 may be a multi-axis accelerometer, such as a 3-axis, 4-axis, 5-axis, 6-axis, etc. The bike IMU 107 may also include multiple gyros. The bike IMU 107 may work in conjunction with a processor or controller to determine the position of the bike and the orientation of the bike relative to a reference point.

[0019] The motorcycle 103 may include a lidar camera 109. The lidar camera 109 may be used to keep track of the rider of the motorcycle 103. The lidar camera 109 may be mounted at various locations along the handlebars of the motorcycle or at other locations facing the rider. The lidar camera 109 may be used to capture images or video of the rider that may be used for various calculations, such as identifying different body parts or movements of the rider. The lidar camera 109 may also be used to focus on the rider's eyes, which may determine eye movement and determine where the rider is looking.

[0020] The motorcycle 103 may include an electronic control unit 111. The ECU 111 may be used to process data collected by the motorcycle sensors and the helmet sensors. The ECU 111 may utilize data received from various IMUs and cameras to perform various position processing and calculations or object recognition. The ECU 111 may communicate with the lidar camera 109 and the forward facing camera 105. For example, data from the IMU may be provided to the ECU 111 to identify position and orientation relative to a reference point. When image data is combined with such calculations, the motion of the motorcycle may be used to identify where the rider is facing or focused. Image data from both the motorcycle forward facing camera and the helmet camera may be compared to determine the relative orientation of the motorcycle and the rider's head. The image comparison may be performed based on sparse features extracted from both cameras (e.g., the lidar camera 109 and the forward facing camera 105). The motorcycle 103 may include a motorcycle central processing unit 113. This allows the system to continuously monitor the rider's attention, posture, position, orientation, contact (e.g., grip on the handlebars), rider slippage (e.g., rider contact with the seat), relationship of the rider to the vehicle, and relationship of the rider to the outside world.

[0021] 2 is an example of a flowchart 200 for rider attention verification. A rider attention verification system can help determine whether the PTW heading and the rider's line of sight are aligned. Such verification can enable the rider's intent to be determined and therefore provide adaptable content for the head mounted display or HUD. The flowchart 200 can be fully or partially implemented in a processor or controller in the helmet, the PTW, or an off-vehicle server (e.g., "cloud").

[0022] Motorcycle data 201 may be collected and provided to other modules. The motorcycle data may include data collected as described with respect to FIG. 1. For example, the motorcycle data may include image data or other data collected from a camera or similar sensor on the motorcycle. The motorcycle data may include image data from a lidar camera or a forward-facing camera. Additionally, the motorcycle data may include any motorcycle IMU data collected.

[0023] Helmet camera data 203 can be collected and provided to other modules. Helmet camera data can include image data and other data collected and collected by a smart helmet as described with respect to FIG. 1. For example, helmet camera data can include image data or other data collected from a helmet camera or similar sensor that can be focused on the rider's face or can face forward to collect image information about the user's environment. Helmet camera data can include image data from a lidar camera or a forward-facing camera. Additionally, helmet camera data can include any motorcycle IMU data collected.

[0024] An image reset 205 may be provided with motorcycle data 201 and helmet camera data 203. The image reset 205 may be the corrections required to correct for IMU drift errors. For example, the motorcycle data and helmet camera data may include image data from a motorcycle camera and a helmet camera. dataare input to the system. The IMU measurements are continuously integrated to obtain an estimate of the position and orientation. Measurement errors therefore result in drift that may be uncompensable. The system can utilize images and other data collected based on where the bike is facing / looking (utilizing orientation and GPS data) to correct for drift. The system visually compares the helmet camera data (e.g., image data) and the bike camera data to identify if the IMU needs to be corrected or adjusted. However, if the images are similar or identical, the integration of the IMU measurements is reset, which clears the effects of accumulated drift errors. Using the IMU alone may result in drift errors, so an image reset improves the estimated orientation to the bike. The image reset 205 may be a hard reset as it is related to the initialization period.

[0025] A visual bias estimator (VBE) 207 is fed with helmet camera data 203 and can use this to correct for IMU bias. The VBE 207 can be used to correct for IMU drift errors that cause overshoot / undershoot of the estimated orientation. For example, the rider may turn at 90 degrees, but the IMU may read the turn as 120 degrees, 80 degrees, or some other angle. The IMU may not properly compensate for errors and biases in the measurements and therefore may not properly read the appropriate measurements. To compensate for biases, other systems or software may identify ground truth (information obtained from a ground survey to verify the results of the aerial survey). In addition to the IMU data, the visual estimator can use helmet camera data 203, which includes image data. Thus, the VBE 207 corrects for errors associated with the IMU overshoot / undershoot by comparing the IMU data with the helmet camera data 203. The VBE 207 can be used to correct for errors associated with the measurements and the IMU due to associated movements.

[0026] IMU measurements 213 may be collected from both the motorcycle and the helmet, and in one embodiment, IMU measurements 213 may be collected only from the helmet.

[0027] An IMU initialization module 209 may be utilized to determine initial conditions for integration of IMU measurements during an initialization period. In addition to the camera data, the IMU initialization module 209 may also be provided with IMU measurement data 213. Additionally, visual bias estimator data may be provided to the IMU integrator 211. The IMU integrator 211 may incorporate the IMU data

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[0028] The IMU integrator 211 can receive data from the IMU initialization module. Measurements collected from both IMUs can be provided to the IMU integrator 211. The IMU integrator 211 integrates data from the IMU measurements and the corrected output from the VBE 207 to correct bias drift (b ω ) can reduce errors caused by the

[0029] Helmet line of sight d h215 may be output as a result of the IMU integrator 211. The gaze direction of the helmet may assist in identifying verification of the rider's attention. For example, if the rider is looking away from an object or vehicle in front of the motorcycle, the system may identify that the rider is looking away from the travel path and may alert the rider using the helmet HUD. The system may also provide contextual information, such as rear view camera information or other details, based on the rider's direction.

[0030] FIG. 3 is an example diagram 300 of a head pose estimation model that can utilize data collected from the helmet and motorcycle. The head pose estimator can help identify how the rider is leaning relative to the bike. The system can consider x, y, and z positions as well as orientation. The head pose estimation system can utilize data collected from sensors on both the helmet and motorcycle to describe the head pose relative to the helmet and PTW. The head pose estimation system may need to estimate the helmet pose relative to the inertial and PTW coordinate systems. Thus, the PTW pose X b Using the ,relative pose between the helmet and the PTW, i.e., X HB =X H *X B -1 can be derived.

[0031] The system can assume that the position of the IMU of the PTW (e.g., motorcycle) is the origin of the PTW coordinate system. A composite state estimator can be constructed from the state of the smart helmet and the PTW state, which can be estimated simultaneously. The state of the system described above is: (1) The position of the PTW, X b , (2) PTW direction q b (3) PTW speed v b , (4) IMU bias of the PTW relative to the inertial coordinate frame b b , (5) Smart helmet position Xh , (6) Smart helmet orientation q h , (7) Smart helmet speed v h , (8) Smart helmet bias b h , and (9) gravity (G) representing the inertial coordinate system. Therefore, the state is X(t) = [X b q b v b b b X h q h v h b h G] can be equal to

[0032] The system can perform a propagation 303 for the previous state X(t-1) based on bike IMU measurements 305 and helmet IMU measurements 307. The propagation may be an updated estimate. Errors in the propagation can be corrected based on updates from the helmet camera, bike camera, ECU and skeleton tracker. A joint estimator can capture correlated measurements between the bike IMU and helmet IMU to provide better results. An iterative estimation such as an extended Kalman filter or nonlinear optimization can be used to estimate the state in real time.

[0033] In step 310, the system can update the state model by aggregating the various data communicated. The updates can be used to correct offsets or off-shots from measurements based on data collected by various sensors on the motorcycle or helmet. For example, the updater can handle various offset errors or other errors by comparing and exchanging data collected from various IMUs and cameras (e.g., images). The image data provides visual landmarks where residuals in the images are minimized to update bias errors of the IMUs. The motorcycle ECU 311 can be used to execute various commands and instructions related to data collected by the motorcycle, including data collected by the bike ECU.

[0034] The front bike camera updater 309 can send images from the bike camera to be used during the update process. In one embodiment, the front camera updater 309 can use data present on the bike and directed towards the rider. In another embodiment, the front camera updater 309 can use a camera facing away from the rider and towards the front of the motorcycle. The helmet camera can update the provided information regarding the rider's line of sight (313). Features are extracted from the image data and compared to find the relative orientation and position of the cameras. These features can also be supported by various IMU measurements.

[0035] The skeleton tracker 315 can be fed with data identifying different skeleton parts of the rider as well as limbs, joints, etc. The skeleton tracker extracts features from the images that represent different parts of the body. These features are associated with a human skeleton model for tracking and updating the system. Once all the data is fed and updated, the system can determine a current state space model 317. The current state space model 317 can identify measurements of the rider's position and the motorcycle's position.

[0036] FIG. 4 is an example diagram 400 of a rider body pose estimator. In a PTW application, the rider's upper body skeleton can be modeled as a tree structure that includes all the body joints above the hip joints. Cameras in the instrument cluster facing the rider can be used to identify the joint locations. The kinematic tree based on the upper body skeleton model can be updated as joint observations are made. Updates from the head pose estimator (e.g., FIG. 3) can be used to further improve the body pose estimation and vice versa.

[0037] The camera 401 may be directed at a rider of the motorized vehicle. The camera 401 may collect images or other data to inspect various parts of the rider's body or other body parts. The camera 401 may be operatively coupled to a processor or controller that assists in identifying body parts via a part extractor 403. The camera 401, which in one embodiment may be mounted on the motorcycle and in other embodiments may be on a helmet, is directed at the rider's body. The processor may include software that is utilized by the part extractor 403. The part extractor 403 may have algorithms or other elements in place that are utilized to compare the image of the data to templates that are utilized to identify body parts. For example, image data collected from the camera may be compared to templates that look at the outline of shapes captured from a person to identify specific body parts. The camera 401 may be utilized in conjunction with the part extractor 403 to identify various extremities (e.g., arms, legs, thighs, forearms, neck, etc.).

[0038] In step 405, the system can determine the rider's head pose. Now, whereas a typical IMU can use data to understand the movement of the rider or motorcycle excluding its relative position with respect to its current environment, head pose software can use these IMU measurements combined with image data to know where the head pose is in relation to the bike. In this way, the head pose estimator can determine where the rider is looking in relation to the bike or the rider's body position.

[0039] In step 407, the camera can identify skeletal tracking of various limbs of the rider's body. Here, the system can identify various limbs in step 403, but then start tracking that particular limb in step 407. For example, the camera can identify an arm and then use the camera to identify where this arm of the user is moving. Thus, such information can be useful to identify if the user has taken their hands off the handlebars of the motorcycle, or if a turn has been excessive by looking at the arms.

[0040] A processor can be utilized to obtain an upper body kinematic model 409. The kinematic tree of the upper body skeletal model can be updated as joint observations are made. For example, the camera 401 can analyze the joints to understand the upper body movements. From there, the various models can be compared to a current analysis of the user's upper body joints.

[0041] The processes, methods or algorithms disclosed herein can be provided or implemented in a processor, controller or computer, which may include any existing programmable or dedicated electronic control unit. Similarly, the 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, persistently stored information in a non-writable storage medium, such as a ROM device, and mutably stored information in a writable storage medium, such as a floppy disk, magnetic tape, CD, RAM device and other magnetic and optical media. The processes, methods or algorithms can also be implemented in software executable objects. Alternatively, the processes, methods or algorithms can be embodied 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 combinations of hardware, software and firmware components.

[0042] Although exemplary embodiments have been described above, these embodiments are not intended to describe all possible forms encompassed by the scope of the claims. The words used herein are words of description rather than limitation, and it is understood that various modifications are possible without departing from the spirit and scope of the disclosure. As described above, features of various embodiments can be combined to form further embodiments of the invention not expressly described or shown. Although various embodiments have been described as offering advantages or being preferred over other or prior art embodiments with respect to one or more desired characteristics, those skilled in the art will recognize that one or more features or characteristics may be compromised to achieve desired overall system attributes depending on the particular application and embodiment. These attributes 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. In this regard, where any embodiment is described as less desirable than other or prior art embodiments with respect to one or more characteristics, these embodiments are not outside the scope of the present disclosure and may be desirable for the particular application.

Claims

1. a transceiver configured to receive vehicle data from one or more sensors disposed on the vehicle; an inertial measurement unit configured to collect helmet motion data for a helmet associated with a rider of the vehicle; a processor coupled to the transceiver and the inertial measurement unit, receiving vehicle data from the one or more sensors disposed on the vehicle via the transceiver; utilizing the vehicle data from the one or more sensors disposed on the vehicle and the helmet motion data from the inertial measurement unit to identify a rider's attention state; A processor programmed to: Equipped with The processor is further configured to determine a body position state of the rider utilizing at least the helmet motion data, vehicle motion data from a vehicle inertial measurement unit disposed on the vehicle, and image data collected from a vehicle camera configured to monitor the rider of the vehicle; The processor includes software that is used with a portion extractor, and the vehicle camera is capable of being used with the portion extractor to identify the extremities of the rider. helmet.

2. The helmet of claim 1 , further comprising a camera configured to identify one or more objects in the vicinity of the vehicle.

3. 10. The helmet of claim 1, further comprising an eye tracking device configured to collect eye tracking data of the rider of the vehicle.

4. 10. The helmet of claim 1, wherein the processor is further configured to determine a head position state utilizing the helmet motion data and vehicle motion data from a vehicle inertial measurement unit disposed in the vehicle.

5. The helmet of claim 1 including a head-up display configured to output a graphical image onto a visor of the helmet.

6. The helmet of claim 1 , wherein the vehicle is a motorcycle or two-wheeled motorized device.

7. The helmet of claim 1 , wherein the processor is further configured to fuse at least the helmet motion data and the vehicle data from the inertial measurement unit of the helmet.

8. 1. A system including a helmet and a vehicle having at least two wheels, a helmet transceiver configured to receive vehicle data from one or more sensors disposed on the vehicle; a helmet inertial measurement unit configured to collect helmet motion data associated with the helmet; a lidar facing camera disposed on the vehicle and configured to monitor a lidar of the vehicle and collect lidar image data; a processor within the helmet coupled to the helmet transceiver and the helmet inertial measurement unit, receiving vehicle data from the one or more sensors located on the vehicle via the helmet transceiver; utilizing the vehicle data from the one or more sensors disposed on the vehicle and the helmet motion data from the helmet inertial measurement unit to identify a rider's attention state; A processor programmed to: Equipped with the processor is further configured to determine a body position state of the rider using at least the helmet motion data, vehicle motion data from a vehicle inertial measurement unit disposed on the vehicle, and image data collected from the lidar-facing camera; the processor includes software utilized by a portion extractor, and the lidar facing camera is utilized in conjunction with the portion extractor to identify extremities of the lidar. system.

9. The system of claim 8 , further comprising a forward-facing camera disposed on the vehicle and configured to monitor an environment proximate the vehicle and collect image data of objects.

10. 10. The system of claim 8, wherein the LIDAR facing camera is configured to collect image data in a vicinity of a LIDAR of the vehicle.

11. The system of claim 10 , wherein the helmet includes a head-up display configured to output a graphical image on a visor of the helmet.

12. The system of claim 10, wherein the vehicle is a motorcycle or two-wheeled motorized device.

13. 11. The system of claim 10, wherein the processor is further configured to determine a head position state utilizing the helmet motion data and vehicle motion data from a vehicle inertial measurement unit disposed in the vehicle.

14. a transceiver configured to transmit motorcycle data from one or more sensors disposed on the motorcycle to the helmet and to receive helmet data from the helmet; a vehicle inertial measurement unit configured to collect vehicle motion data of the motorcycle; a processor coupled to the transceiver and the vehicle inertial measurement unit, receiving helmet data from one or more sensors disposed on the helmet; utilizing the helmet data from the one or more sensors disposed in the helmet and the vehicle motion data from the vehicle inertial measurement unit to identify a rider's attention state; A processor programmed to: a rider-facing camera configured to monitor the rider of the motorcycle; Equipped with the processor is further configured to determine a body position state of the rider using at least helmet motion data collected from a helmet inertial measurement unit disposed on the helmet, vehicle motion data from the vehicle inertial measurement unit, and image data collected from the rider-facing camera; the processor includes software utilized by a portion extractor, and the lidar facing camera is utilized in conjunction with the portion extractor to identify extremities of the lidar. Motor cycle.

15. The motorcycle of claim 14 including a forward-facing camera configured to collect image data in a vicinity of the motorcycle.

16. The motorcycle of claim 14, wherein the lidar-facing camera is further configured to collect image data in the vicinity of the lidar.

17. 15. The motorcycle of claim 14, wherein the processor is further configured to receive helmet motion data collected from a helmet inertial measurement unit and helmet data from the one or more sensors disposed on the helmet, and to utilize the helmet data and helmet motion data to identify an attention state of the rider.

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