Devices and Systems Related to Smart Helmets

Abstract

Apparatuses and systems related to a smart helmet are provided. A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet further includes: an inertial movement unit (IMU) configured to collect helmet movement data of a rider of the vehicle; and a processor in communication with the transceiver and the IMU and programmed to receive vehicle data from one or more sensors located on the vehicle via the transceiver and to determine a rider attention state using the vehicle data from the one or more sensors located on the vehicle and the helmet movement data from the IMU.

Description

Technical Field

[0001] The present disclosure relates to intelligent helmets, such as helmets for motorcycles or other vehicles, where the other vehicles include off-road motorcycles, three-wheeled vehicles, or four-wheeled vehicles such as all-terrain vehicles, etc. Background Art

[0002] Due to resource constraints and limitations for powered two-wheelers (PTWs), continuous monitoring of motorcycle riders can be difficult. In contrast to motor vehicle safety systems, motorcycles may have technologies that are not transferable due to various constraints in PTWs due to environmental and design differences.

[0003] Motorcycles 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 can be mainly used to generate data by the vehicle. The system can provide warning indicators to enable the safety of PTW riders. Summary of the Invention

[0004] According to one embodiment, a helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle. The helmet further 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 vehicle data from one or more sensors located on the vehicle via the transceiver, and to determine a rider attention state using the vehicle data from one or more sensors located 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 located on the vehicle, a helmet inertial measurement unit (IMU) configured to collect helmet motion data of a rider of the vehicle. The motorcycle further includes a rider-facing camera located on the vehicle and configured to monitor the rider of the vehicle and collect rider image data. The system further 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 located on the vehicle via the helmet transceiver, and to determine a rider attention state using the vehicle data from one or more sensors located on the vehicle and the helmet motion data from the IMU.

[0006] According to a third embodiment, a motorcycle includes a transceiver configured to send motorcycle data from one or more sensors located on the motorcycle to a helmet and receive helmet data from the helmet. The motorcycle also includes a vehicle inertial measurement unit (IMU) configured to collect vehicle motion data of a rider of the motorcycle, and a processor in communication with the transceiver and the vehicle IMU. The processor is programmed to receive helmet data from one or more sensors located on the helmet and determine rider attention using the helmet data from one or more sensors located on the vehicle and the vehicle motion data from the vehicle IMU. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is an example of a system design 100 including a smart helmet and a motorcycle.

[0008] Figure 2 is an example of a flowchart 200 for rider attention verification.

[0009] Figure 3 is an example diagram 300 of head pose estimation.

[0010] Figure 4 is an example diagram 400 of rider body pose estimation. DETAILED DESCRIPTION

[0011] Embodiments of the present disclosure are described herein. However, it will be understood that the disclosed embodiments are merely examples, and other embodiments may take various forms and alternative forms. The figures are not necessarily to scale; some features may be enlarged or minimized to show details of particular components. Thus, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching one skilled in the art to employ the embodiments in different ways. As will be understood by one of ordinary skill in the art, various features illustrated and described with reference to any one of the figures may be combined with features illustrated in one or more other figures to produce embodiments that are not explicitly illustrated or described. Combinations of the illustrated features provide representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of the present disclosure may be desirable for a particular application or implementation.

[0012] Continuous monitoring of the rider's state can be crucial for enabling the ARAS to work with an active safety system. The rider's posture, head position, and orientation can be crucial for an active ARAS. Additionally, identifying contacts can enable the implementation of a dynamic rider-bicycle model. In contrast to cars, PTWs may require helmets. Modern smart motorcycle helmets can extend the instrument cluster and dashboard to provide critical information on a head-up display (HUD). Sensors can be added to the helmet to enable tracking of the rider's head pose to help determine the intent of the human driver or to determine what to display on the HUD. The system can allow for continuous monitoring of the rider's attention, posture, contacts, and spatial relationship with the PTW by using vision and inertial sensors on the helmet and PTW.

[0013] The rider's state can be described by the posture of the head (e.g., position and orientation), upper body joints, and contacts relative to the PTW and the world. By using the estimated rider state, it is possible to determine the rider's attention, posture, slip, etc. required for ARAS applications. Rider attention can refer to the direction of the field of view on which the rider's eyes are focused. Rider posture can refer to the positions of the rider's back, neck, shoulders, arms, and other upper body parts. Rider contact can refer to the grip between the rider's hand and the motorcycle's handlebar or other parts. Rider slip can refer to the contact between the rider's bottom and the motorcycle seat. Other rider state information can include the rider-vehicle relationship and the rider-world relationship.

[0014] Figure 1 is an example of a system design 100 that includes a smart helmet 101 and a motorcycle 103. The smart helmet 101 and the motorcycle 103 can include various components and sensors that interact with each other. The smart helmet 101 can focus on collecting data related to the driver's body and head movements. In one example, the smart helmet 101 can include a camera 102. The camera 102 of the helmet 101 can include a primary sensor for position and orientation identification in a moving vehicle. Thus, the camera 102 can face the outside of the helmet 101 to track other vehicles and objects around the rider. The camera 102 may have difficulty capturing the dynamics of such objects and vehicles. In another example, in addition to or instead of the camera 102, the helmet 101 can include a radar or LIDAR sensor.

[0015] The helmet 101 may also include a helmet Inertial Measurement Unit (IMU) 104. The helmet IMU 104 can be used to track the high-dynamic motion of the rider's head. Thus, the helmet IMU 104 can be used to track the direction the rider is facing or the direction the rider is looking. Additionally, the helmet IMU 104 can be used to track sudden movements and other possible issues. The IMU can include one or more motion sensors.

[0016] An Inertial Measurement Unit (IMU) can use a combination of an accelerometer and a gyroscope (sometimes also called a magnetometer) to measure and report the specific forces, angular rates, and sometimes the magnetic field around the subject. IMUs are typically used for control: of aircraft, including unmanned aerial vehicles (UAVs) among many other things; and of spacecraft, including satellites and landers. An IMU can be used as a component of an inertial navigation system used in various vehicle systems. The data collected from the sensors of an IMU can allow a computer to track the motor position.

[0017] An IMU can work by using one or more accelerometers to detect the current acceleration rate and one or more gyroscopes to detect changes in rotational properties such as pitch, roll, and yaw. A typical IMU also includes a magnetometer, which can be used to assist in calibration against directional drift. An inertial navigation system contains an IMU with angular accelerometers and linear accelerometers (for changes in position); some IMUs include gyroscopic elements (for maintaining an absolute angular reference). An angular rate meter measures how the vehicle rotates in space. There can be at least one sensor for each of the three axes: pitch (nose up and down), yaw (nose left and right), and roll (clockwise or counterclockwise from the cockpit). A linear accelerometer can measure the non-gravitational acceleration of the vehicle. Since it can move in three axes (up & down, left & right, forward & backward), there can be a linear accelerometer for each axis. These three gyroscopes are typically placed in a similar orthogonal pattern to measure the rotational position with reference to an arbitrarily chosen 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, and Oz), it can integrate the sensed acceleration, along with the gravity estimate, over time 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 as follows:

[0018]

[0019] is the raw measurement of the IMU in the body frame of the IMU. 、 is the expected correct acceleration and gyro rate measurement. 、 is the bias offset in the accelerometer and gyroscope. , is the noise in the accelerometer and gyroscope.

[0020] The helmet 101 may also include an eye tracker 106. The eye tracker 106 can be used to determine the direction in which the rider of the motorcycle 103 is looking. The eye tracker 106 can also be used to identify a sleepy and fatigued or PTW rider. The eye tracker 106 can identify various parts of the eye (such as the retina, cornea, etc.) to determine where the user is saccading. The eye tracker 106 can include a camera or other sensors to assist in tracking the rider's eye movements.

[0021] The helmet 101 may also include a helmet processor 108. The helmet processor 107 can be used for sensor fusion of data collected by various cameras and sensors of both the motorcycle 103 and the helmet 101. In other embodiments, the helmet may include one or more transceivers for short-range communication and long-range communication. The short-range communication of the helmet can include communication with the motorcycle 103 or other vehicles and objects in the vicinity. In another embodiment, the long-range communication can include communication to a non-airborne server, the Internet, the "cloud", cellular communication, etc. The helmet 101 and the motorcycle 103 can communicate with each other using a wireless protocol implemented by transceivers located on both the helmet 101 and the motorcycle 103. Such protocols can include Bluetooth, Wi-Fi, etc. The helmet 101 may also include a head-up display (HUD) for outputting graphical images on the visor of the helmet 101.

[0022] The motorcycle 103 may include a forward-facing camera 105. The forward-facing camera 105 can be located on the headlight or other similar area of the motorcycle 103. The forward-facing camera 105 can be used to help identify where the PTW is moving forward. In addition, the forward-facing camera 105 can identify various objects or vehicles in front of the motorcycle 103. Therefore, the forward-facing camera 105 can assist various safety systems, such as intelligent cruise control or collision detection systems.

[0023] The motorcycle 103 may include a single-bike IMU 107. The single-bike IMU 107 can be attached to the headlight or other similar area of the PTW. The single-bike IMU 107 can collect inertial data that can be used to understand the movement of the single bike. The single-bike IMU 107 can be a multi-axis accelerometer such as a three-axis, four-axis, five-axis, six-axis, etc. The single-bike IMU 107 may also include multiple gyroscopes. The single-bike IMU 107 can work with a processor or controller to determine the position of the single bike relative to a reference point and its orientation.

[0024] The motorcycle 103 may include a rider camera 109. The rider camera 109 may be used to keep track of the rider of the motorcycle 103. The rider camera 109 may be mounted in various positions along the motorcycle handlebars or in other positions facing the rider. The rider camera 109 may be used to capture images or videos of the rider, which are in turn used for various calculations, such as identifying various body parts or movements of the rider. The rider camera 109 may also be used to focus on the rider's eyes. Accordingly, eye gaze movement may be determined to find out where the rider is looking.

[0025] The motorcycle 103 may include an electronic control unit 111. The ECU 111 may be used to process data collected by sensors of the motorcycle and data collected by sensors of the helmet. The ECU 111 may utilize the data received from various IMUs and cameras to process and calculate various positions or perform object recognition. The ECU 111 may communicate with the rider camera 109 and the forward-facing camera 105. For example, data from the IMU may be fed into the ECU 111 to identify the position and orientation relative to a reference point. When the image data is combined with such calculations, the movement of the bicycle may be used to identify where the rider is facing or focusing. Image data from both the forward-facing camera on the bicycle and the camera on the helmet are compared to determine the relative orientation between the bicycle and the rider's head. Image comparison may be performed based on alternative features extracted from two cameras (e.g., the rider camera 109 and the forward-facing camera 105). The motorcycle 103 may include a bicycle central processing unit 113. Thus, the system may continuously monitor rider attention, posture, position, orientation, contact (e.g., grip on the handlebars), rider slip (e.g., contact between the rider and the seat), rider-vehicle relationship, and rider-world relationship.

[0026] Figure 2 is an example of a flowchart 200 for rider attention verification. The rider attention verification system may help determine whether the forward direction of the PTW and the rider's field of view direction are aligned. This verification may allow determination of the rider's intent and thus provide adaptive content on a head-mounted display or HUD. The flowchart 200 may be implemented in whole or in part on a processor or controller of the helmet, PTW, or off-board server (e.g., "the cloud").

[0027] Motorcycle data 201 may be collected and provided to other modules. Motorcycle data may include data aggregated and collected as discussed in reference Figure 1 as discussed. For example, motorcycle data may include image data or other data collected from cameras or similar sensors in the motorcycle. This may include image data from the rider camera or the forward-facing camera. Additionally, this may include any motorcycle IMU data collected.

[0028] Data of the helmet camera 203 can be collected and provided to other modules. The helmet camera data can include images and other data aggregated and collected by the smart helmet as discussed in reference Figure 1 For example, the helmet camera data can include image data or other data collected from a camera or similar sensor in the helmet, which can focus on the rider's face or can be forward-facing to collect image information about the user's environment. This can include image data from the rider camera or the forward-facing camera. Additionally, this can include any motorcycle IMU data collected.

[0029] The image reset 205 can be fed motorcycle data 201 and helmet camera data 203. The image reset 205 can be a correction required for correcting the drift error of the IMU. For example, the bicycle data and the helmet camera are input into the system, which can include image data from the camera on the bicycle as well as the camera on the helmet. The IMU measurements are continuously integrated to obtain position and orientation estimates. Therefore, measurement errors can cause irrecoverable drift. The system can utilize the images and other data (using orientation and GPS data) collected based on where the bicycle is facing / looking to correct the drift. The system will visually compare the helmet camera data (such as image data) and the bicycle camera data to determine if the IMU needs to be corrected or adjusted. However, if the images are similar or identical, the IMU measurement integration is reset, which will clear the effect of the accumulated drift error. The image reset improves the orientation relative to the bicycle estimate because using only the IMU can cause drift errors. The image reset 205 can be a hard reset because it is associated with an initialization period.

[0030] The Visual Bias Estimator (VBE) 207 can be fed helmet camera data 203 and utilize it to correct the IMU bias. The VBE 207 can be used to correct the 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 that you are turning at 120 degrees, 80 degrees, or some other angle. The IMU may not correctly compensate for the errors and biases in them to correctly read the appropriate measurements. To compensate for the bias, another system or software can identify the ground truth. The visual estimator can utilize the helmet camera data 203, including the image data along with the IMU data. Therefore, the VBE 207 corrects the errors associated with IMU overshoot / undershoot by comparing the IMU data with the helmet camera data 203. The VBE 207 can be used to correct the errors associated with the IMU and the measurements from the associated movement.

[0031] IMU measurements 213 can be collected from both the motorcycle and the helmet. In one example, the IMU measurements 213 can be collected only from the helmet.

[0032] The IMU initialization 209 can be used to determine the initial conditions for integrating IMU measurements at the initialization period. In addition to camera data, the IMU initialization 209 module can also be fed IMU measurement data 213. Additionally, the visual bias estimator data can be fed into the IMU integrator. The IMU integrator 211 can aggregate the IMU data ( ) and image data to help identify the helmet field of view direction ( ). For example, if the viewing direction is assumed to be along the yaw inertial axis, it is defined as follows .

[0033]

[0034] The IMU integrator 211 can receive data from the IMU initialization module. The measurements collected from the two IMUs can be fed into the IMU integrator 211. The IMU integrator 211 can integrate the data from the IMU measurements and the correction output from the VBE 207 to reduce the error caused by the bias drift ( ) in the data from the IMU measurements.

[0035] Helmet field of view direction 215 can be output as a result of the IMU integrator 211. The helmet field of view direction can help identify rider attention verification. For example, if the rider is looking away from an object or vehicle in front of the motorcycle, the system can realize that the rider is looking away from the driving path and use the HUD of the helmet to warn the rider. The system can also provide context information based on the rider's direction, such as rearview camera information or other details.

[0036] Figure 3 is an example diagram 300 of a head pose estimation model that can be utilized with the data collected from the helmet and the motorcycle. Head pose estimation can help identify how the rider is leaning into the bike. The system can look not only at the orientation but also the x, y, z positions. The head pose estimation system can utilize the data collected from the sensors on both the helmet and the motorcycle to describe the head pose relative to the helmet and the PTW. The head pose estimation system may need to estimate the pose of the helmet relative to the inertial coordinate system and the PTW coordinate system. Therefore, the PTW pose X b can be used to derive the relative pose between the helmet and the PTW:

[0037]

[0038] The system can assume that the IMU position on the PTW (e.g., a motorcycle) may be the origin of the PTW coordinate system. The combined state estimator can consist of the states from the smart helmet, and the PTW state can be estimated simultaneously. The states of the system described above can consist of: (1) PTW position X b ; (2) PTW orientation q b (3) PTW velocity V h ; (4) PTW IMU bias relative to the inertial coordinate frame b b ; (5) smart helmet position X h ; (6) smart helmet orientation q h ; (7) smart helmet velocity v h ; (8) smart helmet bias b h and (5) represents the gravity of the inertial coordinate system ( G ). Therefore, the state can be equal to:

[0039]

[0040] The system can perform propagation 303 based on the bicycle IMU measurement 305 and the helmet IMU measurement 307 on the previous state . The propagation can be an updated estimate. The error in the propagation can be corrected based on updates from the helmet camera, bicycle camera, ECU, and skeleton tracker. The joint estimate can capture the measurements associated between the bicycle IMU and the helmet IMU and provide better results. Iterative estimators such as the extended Kalman filter or nonlinear optimization can be used to estimate the state in real time.

[0041] At step 310, the system can update the state model by aggregating various data of the propagation. The update can be used to correct any offsets or offshoots from the measurements based on the data collected by various sensors on the motorcycle or the helmet. For example, the update can process various offset errors or other errors by comparing and exchanging the data (e.g., images) collected from various IMUs and cameras. The image data provides visual landmarks, and the residue in the image is minimized to update the IMU bias error. The motorcycle ECU 311 can be used to execute various commands and instructions related to the data collected by the motorcycle (including the data collected by the bicycle ECU).

[0042] The forward bike camera update 309 can send images from the bike camera to be utilized during the update process. In one embodiment, the forward camera update 309 can utilize data present on the bike and facing the rider. In another embodiment, the forward camera update 309 can utilize a camera that faces away from the rider and is in front of the motorcycle. The helmet camera can update the information provided by 313 regarding the rider's field of view. Features are extracted from the image data and matched to find the relative orientation and position of the camera. Various IMU measurements can also support this feature.

[0043] The backbone tracker 315 can be fed data identifying different bone parts of the rider as well as limbs, joints, etc. The tracked backbone extracts features from images representing different parts of the body. These features are associated with a human skeletal model to track and update the system. Once all the data is fed and updated, the system can determine the current state space model 317. The current state space model 317 can identify measurements of the rider's and the motorcycle's positions.

[0044] Figure 4 is an example diagram 400 of rider body pose estimation. In a PTW application, the rider's upper body skeleton can be modeled as a tree structure including all body joints above the hip joint. A camera facing the rider above the instrument cluster can be used to determine the positions of the joints. When a joint is observed, the motion tree based on the upper body skeleton model can be updated. Updates from head pose estimation (e.g., Figure 3 ) can be used to further refine the body pose estimation and vice versa.

[0045] The camera 401 can be aimed at the rider of a motor vehicle. The camera 401 can collect images or other data to examine various parts of the rider's body or other body parts. The camera 401 can work with a processor or controller that helps identify body parts via part extraction 403. In one embodiment, the camera 401 can be mounted on the motorcycle, and in another embodiment, the camera 401 can be mounted on the helmet but pointed at the rider's body. The processor can include software for part extraction 403. The part extraction 403 module can have algorithms or other elements in place that are used to compare the data image with a template for identifying body parts. For example, the image data collected from the camera can be compared with a template that looks at the outline of a shape taken from a person to identify certain body parts. The camera 401 can be used in conjunction with the part extraction module 403 to identify various limbs (e.g., arms, legs, thighs, forearms, neck, etc.).

[0046] At step 405, the system can determine the rider's head pose. Thus, while a typical IMU can use data to understand movement without the relative position in terms of the rider or the actual environment of the motorcycle, head pose software can be used to combine such IMU measurements with image data to know where the head pose is relative to the bicycle. Thus, head pose estimation can determine where the rider is looking relative to the bicycle or the rider's body position.

[0047] At step 407, the camera can identify the bone tracking of the various limbs of the rider's body. Thus, the system can identify the various limbs at step 403, but then start tracking those specific limbs at step 407. For example, the camera can identify an arm and then use the camera to determine where the user's arm is moving. Thus, such information can be useful for determining whether the user has let go of the motorcycle handlebars or turned too sharply by looking at the arm.

[0048] A processor can be used to obtain the upper body motion model 409. When observing joints, the motion tree of the upper body bone model can be updated. For example, the camera 401 can analyze the joints and see the movement of the upper body. From there, various models can be compared with the current analysis of the joints in the user's upper body.

[0049] The processes, methods, or algorithms disclosed herein can be delivered to and implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored in many forms as data and instructions executable by a controller or computer, including but not limited to information permanently stored on a non-writable storage medium such as a ROM device and information variably stored on 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 a software-executable object. Alternatively, 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, can be used to embody the processes, methods, or algorithms, in whole or in part.

[0050] Although the exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms covered by the claims. The words used in the specification are words of description rather than limitation, and it should be understood that various changes may be made without departing from the spirit and scope of the present disclosure. As previously mentioned, the features of the various embodiments may be combined to form additional embodiments of the invention that may not be explicitly described or illustrated. Although the various embodiments may have been described as providing advantages over other embodiments or prior art implementations in one or more desired characteristics or being preferred thereto, those of ordinary skill in the art recognize that one or more features or characteristics may be compromised depending on the specific application and implementation to achieve the desired overall system attributes. These attributes may include, but are not limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, suitability, weight, manufacturability, ease of assembly, etc. Accordingly, to the extent that any embodiment is described as less desirable than other embodiments or prior art implementations in one or more characteristics, these embodiments are not outside the scope of the present disclosure and may be desirable for a particular application.

Claims

1. A helmet, comprising: A transceiver configured to receive vehicle data from one or more sensors located on a vehicle; An inertial movement unit configured to collect helmet movement data of the helmet associated with a rider of the vehicle; And A processor in communication with the transceiver and the inertial movement unit and programmed to: Receive vehicle data from one or more sensors located on the vehicle via the transceiver; Utilize the vehicle data from one or more sensors located on the vehicle and the helmet movement data from the inertial movement unit to determine a rider attention state; And Utilize the vehicle data from one or more sensors located on the vehicle and the helmet movement data from the inertial movement unit to determine a head pose estimate identifying how the rider is tilting onto the vehicle.

2. The helmet according to claim 1, wherein, The helmet further includes a camera configured to identify one or more objects near the vehicle.

3. The helmet according to claim 1, wherein, The helmet further includes an eye tracking unit configured to collect eye tracking data of the rider of the vehicle.

4. The helmet according to claim 1, wherein, The processor is further configured to utilize the helmet movement data and the vehicle movement data from a vehicle inertial movement unit located on the vehicle to determine a head position state.

5. The helmet according to claim 1, wherein, The processor is further configured to determine a rider body position state at least by utilizing the helmet movement data, the vehicle movement data from a vehicle inertial movement unit located on the vehicle, and the image data collected from a vehicle camera configured to monitor the rider of the vehicle.

6. The helmet according to claim 1, wherein, The helmet includes a heads-up display configured to output a graphic image on a visor of the helmet.

7. The helmet according to claim 1, wherein, The vehicle is a motorcycle or an electric two-wheeled unit.

8. The helmet according to claim 1, wherein, The processor is further configured to at least fuse the vehicle data and the movement data from the inertial movement unit of the helmet.

9. A system including a helmet and a vehicle having at least two wheels, comprising: A helmet transceiver configured to receive vehicle data from one or more sensors located on the vehicle; A helmet inertial movement unit configured to collect helmet movement data associated with the helmet; A rider-facing camera located on the vehicle and configured to monitor the rider of the vehicle and collect rider image data; and A processor in the helmet in communication with the helmet transceiver and the helmet inertial movement unit and programmed to: Receive vehicle data from one or more sensors located on the vehicle via the helmet transceiver; Utilize the vehicle data from one or more sensors located on the vehicle and the helmet movement data from the helmet inertial movement unit to determine a rider attention state; And Utilize the vehicle data from one or more sensors located on the vehicle and the helmet movement data from the helmet inertial movement unit to determine a head pose estimate identifying how the rider is tilting onto the vehicle.

10. The system according to claim 9, wherein The system further includes a forward-facing camera located on the vehicle, and the forward-facing camera is configured to monitor the environment approaching the vehicle and collect object image data.

11. The system according to claim 9, wherein, The camera facing the rider is configured to collect image data of the rider approaching the vehicle.

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

13. The system according to claim 11, wherein, The vehicle is a motorcycle or an electric two-wheeled unit.

14. The system according to claim 11, wherein, The processor is further configured to determine the body position state of the rider by at least utilizing the helmet motion data, the vehicle motion data from a vehicle inertial movement unit located on the vehicle, and the image data collected from a vehicle camera configured to monitor the rider of the vehicle.

15. The system according to claim 11, wherein, The processor is further configured to determine the head position state by utilizing the helmet motion data and the vehicle motion data from a vehicle inertial movement unit located on the vehicle.

16. A motorcycle, comprising: A transceiver configured to send motorcycle data from one or more sensors located on the motorcycle to the helmet and receive helmet data from the helmet; A vehicle inertial movement unit configured to collect vehicle motion data of the rider of the motorcycle; And A processor in communication with the transceiver and the vehicle inertial movement unit and programmed to: Receive helmet data from one or more sensors located on the helmet; Determine rider attention by utilizing the helmet data from one or more sensors located on the helmet and the vehicle motion data from the vehicle inertial movement unit; and Determine a head pose estimate indicating how the rider is leaning onto the motorcycle by utilizing the helmet data from one or more sensors located on the helmet and the vehicle motion data from the vehicle inertial movement unit.

17. The motorcycle according to claim 16, wherein, The motorcycle includes a forward-facing camera configured to collect image data of the motorcycle.

18. The motorcycle according to claim 16, wherein, The motorcycle includes a camera facing the rider configured to collect image data of the rider.

19. The motorcycle according to claim 16, wherein, The processor is further configured to determine the body position state of the rider by at least utilizing the helmet motion data collected from a helmet inertial movement unit, the vehicle motion data from the vehicle inertial movement unit, and the image data collected from a vehicle camera configured to monitor the rider of the vehicle.

20. The motorcycle according to claim 16, wherein, The processor is further configured to receive the helmet motion data collected from a helmet inertial movement unit and the helmet data from one or more sensors located on the helmet, and determine rider attention by utilizing the helmet data and the helmet motion data.

Images