COPYRIGHT MATERIAL

[0001] A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in a Patent Office patent file or record, but otherwise reserves all copyright rights whatsoever.

TECHNICAL FIELD

[0002] This invention relates to automobile safety. In particular, it relates to motorcycle safety.

BACKGROUND

[0003] Pro-active safety eco-systems do not exist in the motorcycle/ personal mobility industry today. Traction control, anti-lock, braking, lane keep assist, and others are examples of reactive safety systems whereby a loss of control and/ or stability has occurred with the vehicle and now the system acts to regain control and/ or stability. Further, safety systems are isolated to protecting a lone vehicle and the status of the vehicle is kept local without any ability to communicate this information to other vehicles and/ or systems.

SUMMARY

[0004] The present invention relates to safety devices and systems for automotive, motorcycle and other personal mobility devices (two or multi-wheel).

[0005] Disclosed herein is a system for enhancing motorcycle safety comprising: a processor; a front camera operably connected to the processor; a rear camera operably connected to the processor; a front radar operably connected to the processor; a rear radar operably connected to the processor; a front environmental sensor operably connected to the processor; a rear environmental sensor operably connected to the processor; a haptic alert device operably connected to the processor; computer readable memory storing computer readable instructions, which, when executed by the processor cause the processor to obtain data from said cameras, said radars and said sensors, analyze said data, determine using said analysis that there is a threat to the motorcycle safety, and activate the haptic alert device.

[0006] In embodiments, the system comprises a server connected to the processor and configured to: receive said data; modify a threat prediction algorithm using said data; communicate the modified threat prediction algorithm to the processor.

[0007] In embodiments, the system comprises an application installed on a mobile electronic communications device in communication with the processor; configured to verify an operator of the motorcycle before communicating the modified threat prediction algorithm.

BRIEF DESCRIPTION OF DRAWINGS

[0008] FIG. 1 shows modules that make up the motorcycle safety system.

[0009] FIG. 2 shows crowd-sourced software updates.

[0010] FIG. 3 explains the features of the motorcycle safety system.

[0011] FIG. 4 is a mobile device with screen shot giving access to safety feature updates.

[0012] FIG. 5 is the mobile device with a screen shot showing vehicle location and status.

[0013] FIG. 7 shows a screen display after impact detection.

[0014] FIG. 8 is a live view from the motorcycle.

[0015] FIG. 9 is a notifications screen.

[0016] FIG. 10 is an enlarged, alternate rendering of FIG. 8.

[0017] FIG. 1 1 is an enlarged, alternate rendering of FIG. 9. [0018] FIG. 12 is a flowchart showing the main steps the motorcycle safety system takes.

[0019] FIG. 13 is a block diagram of the system according to an embodiment of the present invention.

DESCRIPTION

[0020] Embodiments of the system and method of the present invention address one or more of the following problems:

[0021] There are no mechanisms available on motorcycles that can communicate to the rider of the potential and/ or immediate threats surrounding him/her.

[0022] Safety technology that is local to the motorcycle does not have the means to improve and/or be updated wirelessly.

[0023] Operators of motorcycles have no mechanism to interface to the vehicle remotely by way of an application installed on the user's mobile device or through a remote terminal to adjust/receive vehicle settings, safety detection thresholds, safety feedback settings, real-time on-board camera video.

[0024] There is no infrastructure to support uploading of data and/or information gathered from a motorcycles (that can share data/info) to a remote repository.

[0025] There is no infrastructure to gather data from multiple vehicles sequentially and/or concurrently.

[0026] There are no systems available on motorcycles that can predict accidents based on the environment, detected objects, and perceived threat sensor data.

[0027] There are no device(s) installed on a motorcycle that can gather data and/or information from vehicle sensors, force sensors, environmental sensors, radar, and LiDAR devices to determine: a) presence b) severity c) direction and d) priority of threats around the motorcycle and its operator.

[0028] A rider can not authenticate, authorize, and control the software update process for their vehicle through an application installed on a remote mobile device. [0029] The invention addresses the problems above by creating a motorcycle or personal mobility safety eco-system consisting of computing device, network of sensors, wireless connectivity, mobile device application, cloud storage, and an artificial intelligence or machine learning server to provide local and remote processing of data from the vehicle, rider/operator (via vehicle sensors), and environmental sensors.

[0030] The invention detects, predicts, and prevents potential vehicular accidents from occurring by detecting and combining the behaviours of objects, environment, vehicle dynamics, and operator state to provide a series of alerts, messages, and user- initiated controls to the operator/driver/rider of the vehicle who may initiate actions in a manner to prevent and/or avoid the threat.

[0031] FIG. 1 shows modules that make up the Rift™ motorcycle safety system. These include: Force pressure sensors (to derive driver intent); Haptic communication language; Raw data collection / transmission prioritization (based on need/use case); Scenario modelling and threat prediction from reverse engineering behavioural cues on the street; Edge computing for low latency data processing; and Rider controlled safety update authentications.

[0032] FIG. 2 - Crowd-sourced SW updates.

[0033] FIG. 3 explains the features of the Rift™ Connect system. Icons are white figures on solid red circles.

[0034] LTE CONNECTION: Damon uses a persistent LTE connection to keep you connected with your motorcycle at all times, even when you're miles away.

[0035] DEEP LEARNING: Through steering feedback, aggregate rider behavior collection and other rider inputs, Rift™ is constantly improving its threat detection capability. With constant over air updates, the onboard neural network lets Rift™ vision systems see and respond to more threats over time, keeping you ever safer on the road. [0036] LOCATION TRACKING: Instead of relying on your phone, Damon's built-in GPS engine can tell you exactly where your motorbike is at all times, -even if you aren't on it. Never worry about your bike being stolen again.

[0037] 6-AXIS ACCELEROMETER: With the Rift™ Connect, Damon can tell if your motorcycle has been bumped, towed, or if someone is even sitting on it when you're not there. It also uses your lean angle, pitch and other data points to get to know your unique riding style.

[0038] OWNER AUTHENTICATION: Using your own thumb print, Damon ensures all software updates to the motorcycle are permitted only by the owner. With each update, new feature training tutorials are delivered right to the Rift™ app on your phone.

[0039] DATA PRIVACY: Damon privacy policy is the best in the business. We will never divulge consumer or personal information of any kind about our customers to any party for any reason without explicit permission or user initiation.

[0040] ONBOARD SENSORS: Rift™ Connect collects and batches visual data of up to 360° from its onboard sensors. By combining this data with rider and motorcycle inputs, it can predict and help prevent more accidents over time.

[0041] SECURITY: Damon uses the highest security solutions available, employing 256-bit encryption with secure data access via Authentication, Authorization, and Verification mechanisms.

[0042] FIG. 4 is a mobile device with screen shot giving access to safety feature updates.

[0043] FIG. 5 is the mobile device with a screen shot showing vehicle location and status. The start (black flag), finish (checkered flag) and current location (motorcycle icon) are shown on a map. The time elapsed is 00:35:56, the distance is 2.4km and the maximum speed is 60km/h. Various navigation icons are shown for navigation within the app. FIG. 6 shows the same view with more map detail.

[0044] FIG. 7 shows a screen displaying "Impact Detected"; "120 seconds

remaining"; and "Your location & medical information will be sent to the nearest ambulance service unless you confirm that you are OK and do not require assistance". An OK button is displayed below the text.

[0045] FIG. 8 is a live view from the motorcycle. Video clips can be managed and shared to social media profiles.

[0046] FIG. 9 is a notifications screen, showing, from top to bottom: "Trip Started 8 hours ago"; "Trip completed 5 hours ago"; "Disturbance detected 2 hours ago";

"Battery life good"; "Theft alert 2 hours ago".

[0047] FIG. 10 is an enlarged, alternate rendering of FIG. 8.

[0048] FIG. 1 1 is an enlarged, alternate rendering of FIG. 9.

[0049] FIG. 12 is a flowchart showing the main steps the motorcycle safety system takes.

[0050] FIG. 13 is a block diagram of the system according to an embodiment of the present invention.

[0051] The features of an embodiment of the invention are as follows:

[0052] 1 ) End-to-end motorcycle or personal mobility safety platform that includes: a) local sensor and computing devices, b) cloud infrastructure to support two-way data transfers, and c) an artificial intelligence or machine learning server to develop, train, and deploy safety algorithms.

[0053] 2) An on-board, independent device capable of communicating with external vehicle processing units, vehicle sensors, mobile devices, wireless modules, geo- location devices, and remote server(s) facilitating inter-transfer of data from these devices.

[0054] 3) A system that can identify and detect traffic threats to the vehicle and its operator in a 360 degree circle. [0055] 4) The on-board device communicates to a mobile application to enable over- the-air software updates, vehicle settings, safety settings, real-time video, and travel/ trip information.

[0056] 5) Creates an extensible neural network of sensors including, but not limited to, off-the-shelf commercial automotive, consumer grade, and industrial artificial intelligence or machine learning devices that will communicate and collate their data packaged in a bundle that is transported through an automotive CANBUS protocol to the central processing unit.

[0057] 6) The system detects its surroundings and objects around the vehicle and rider to identify, classify, and prioritize the level of threat of these items to predict the likelihood of an occurrence of an accident.

[0058] 7) A visual and haptic threat communication mechanism that directs the rider the direction and severity of the threat.

[0059] Referring to FIG. 13, the system contains three main parts:

[0060] A) An on-board vehicle mounted system (1 ) built from a Damon central processing unit (1 A), front and rear cameras (1 B, 1 H), front and rear radar (1 C, 1 G), and front and rear environmental sensors (1 D, 1 F). The Damon CPU also contains an optional port to connect to the motor vehicle/ personal mobility device's factory CPU (1 E).

[0061] Intercommunication between these devices are through a CANBUS,

Bluetooth, WiFi, and serial interfaces and protocols.

[0062] B) Damon Mobile Application (2) installed on rider's personal communication device or tablet.

[0063] Communication from the mobile application to the Damon CPU will be through WiFi and/ or Bluetooth and/ or Bluetooth Low Energy protocols (5).

[0064] C) Damon Cloud Service (3) [0065] Communication from the Damon Mobile Application and/ or the Damon CPU is conduct through a cellular network protocol (7).

[0066] D) Damon Artificial Intelligence/ Machine Learning/ Data Vault Server (4).

[0067] The Damon Cloud Service resides and operates inside the Damon AI/ML/Data Vault server and external communications are handled through a local area network (8) connection.

[0068] The system operates with the Damon CPU communicating with the sensors, cameras, and radar at a refresh rate no greater than 100ms to gather object identity, object tracking, ambient temperature, humidity, and vision data (vision data refresh rates may vary if the user requires real-time recording of their trip).

[0069] The information gathered are processed for threat detection from the forward, aft, and blind side positions around the vehicle. To determine the severity of the threat, detected objects are tracked to provide speed, location (x, y relative to rider vehicle), trajectory, rider vehicle speed and trajectory, and the likelihood of intersecting with rider's vehicle.

[0070] If an immediate threat is detected, the Damon CPU provides a signal to the visual and haptic mechanism (1 1, 1 J, located on vehicle) to activate the system to provide directional awareness of the threat to the rider.

[0071] Data collected from the environment, rider, and vehicle are uploaded to the Damon Cloud (3) for data storage (4). An artificial intelligence or machine learning engine (4) is used to derive algorithms and models for accident threat prediction based on these variables.

[0072] The Damon Mobile Application (2) provides an additional interface to the vehicle to adjust, tune, and set vehicle ride characteristics and the sensitivity of safety detection range and sensitivity as well as the alert mechanism (1 1, 1 J) vibration and LED intensity and frequency. [0073] An added feature of the Damon Mobile Application is to allow the user to authenticate and authorize over-the-air updates from the Damon Cloud to the motorcycle via thumbprint, facial recognition, or text password entry.

Variations

[0074] Variations of the invention can be employed in automotive, marine and aviation applications.

[0075] The device is mounted remotely and can interface to the vehicle wirelessly performing the same function as described in the fixed vehicle mount variant.

[0076] For example, in one embodiment, the system operates from the rider's mobile phone device to provide low level and simple object detection zone basis where any object entering the zone will be considered an immediate threat and a subsequent alert to the rider (via visual and/or haptic mechanisms).