A braking system and mehod for two-wheeled vehicles

Abstract

The invention pertains to a braking system and method for two-wheeled vehicles such as scooters, e-scooters, bikes, e-bikes, motorbikes, e-motorbikes, enhanced with AI support. The invention integrates two physical brakes: one manually operated by the user and the other governed by an AI model. Key inputs for the system include measurements from a gyroscope (10), accelerometer (9), speed sensors, and data on the engagement level of rear brake (5). This array of data is fed into an AI algorithm, which analyses the information to determine the optimal application force for front brake (6).

Description

[0001] A BRAKING SYSTEM AND MEHOD FOR TWO-WHEELED VEHICLES

[0002] Technical Field

[0003] The present invention relates to a braking system and method for two-wheeled vehicles, enhanced with artificial intelligence support.

[0004] Background

[0005] In two-wheeled vehicles, riders speed up using throttle and slow down using front and rear brakes. Using them the wrong way can be risky. Different types of vehicles need different ways of braking. For example, on scooters and bikes, it's usually better to use the rear brake more. This is because hitting the front brake too hard can make the bike shakily, which might throw the rider off.

[0006] Motorcycles, on the other hand, depend more on the front brake for quick stopping. If used right, the front brake can stop a motorcycle fast and safely. But if you use the rear brake wrongly, especially when going fast or in a hurry, it can be very dangerous. The rear wheel might lock up or the bike could flip over.

[0007] It's also hard for both new and experienced riders to know how much to press each brake, especially in surprise or emergency situations. This might lead to braking too little, not stopping in time, or braking too hard, causing the rider to lose control.

[0008] The application numbered EP1982884A1 enables, with respect to a vehicle where lifting of the vehicle body easily occurs to a relatively large extent even in a relatively early stage when rear wheel lifting is detected, more rapid and reliable securement of the safety of the vehicle body with respect to that rear wheel lifting. When lifting of a rear wheel is detected, in contrast to what has conventionally been the case, the brake pressure of a front wheel is immediately reduced regardless of whether or not there is occurrence of skidding of the front wheel, so lifting of the entire vehicle body is rapidly and reliably controlled and prevented particularly in a vehicle whose entire vehicle body is easily lifted even by slight rear wheel lifting. However, the application is insufficient to provide the necessary data for impeccable braking during usual driving and to present the decision-making artificial intelligence model.

[0009] So, there's a big need for a better way to brake on two-wheeled vehicles. This new way should make braking safer and cut down the chances of using the brakes wrong.

[0010] Summary

[0011] The invention pertains to a braking system for two-wheeled vehicles such as scooters, e-scooters, bikes, e-bikes, motorbikes, e-motorbikes, enhanced with artificial intelligence (Al) support. The braking system integrates two physical brakes: one manually operated by the user and the other governed by an artificial intelligent model. Key inputs for the system include measurements from a gyroscope, accelerometer, speed sensors, and data on the engagement level of rear brake. This array of data is fed into a machine learning algorithm, which analyses the information to determine the optimal application force for front brake.

[0012] It is aimed to prevent misuse of brakes and ensure optimum braking with artificial intelligence support for two-wheeled vehicles. The data to train artificial intelligence model is collected while the experienced user is riding (data collection phase). The artificial intelligence model is trained with the collected data and the trained models are distributed to all vehicles (Al training and deploying phase).

[0013] The model within the system receives inputs such as the degree of brake engagement, vehicle speed, accelerometer, and gyroscope data. Based on these inputs, the model calculates the optimal amount of pressure to apply to the auxiliary brake (Al braking phase). This process occurs in rapid succession, with calculations being updated every few milliseconds. The outcomes of these calculations are then communicated to the ECU, which in turn sends commands to a step motor. This motor adjusts the brake calliper accordingly, ensuring that braking is performed efficiently and with the support of Al technology. Brief Description of the Figures

[0014] Figure 1 shows the block diagram of the braking system subject to the invention.

[0015] Figure 2 shows the input-output block diagram of the electronic control unit in the braking system.

[0016] Figure 3 shows data collection phase of the present invention.

[0017] Figure 4 shows Al training and deploying phase of the present invention.

[0018] Figure 5 shows Al braking phase according to gathered data in short periods in the present invention.

[0019] Part References

[0020] 1 . Screen

[0021] 2. Throttle

[0022] 3. Rear brake handle

[0023] 4. Battery

[0024] 5. Rear brake

[0025] 6. Front brake

[0026] 7. Motor control unit

[0027] 8. Electronic control unit

[0028] 9. Accelerometer

[0029] 10. Gyroscope

[0030] 1 1 . Servo motor

[0031] Detailed Description

[0032] The present invention given in Figure 1 offers a braking system and method for twowheeled vehicles, enhanced with artificial intelligence. The braking system comprises an accelerometer (9) that measures acceleration on X, Y and Z axis and a gyroscope (10) that measures orientation and angular velocity. A hall sensor included in a rear brake handle (3) gives brake-tightened rate as feedback. An electronic control unit (8) makes an artificial intelligent supported decision on applying front brake (6), how long it will be applied and how much the front brake (6) will be tightened, when a user applies rear brake (5) on the vehicle, according to input data including acceleration, orientation, angular velocity, speed, and rear brake (5) tightening rate. A motor control unit (7) determines and sends speed and power consumption data to the electronic control unit (8) and manages a servo motor (11 ) that applies the brake decision by squeezing a front calliper.

[0033] In Figure 2, the throttle (2), rear brake handle (3), motor control unit (7), accelerometer (9), gyroscope (10), servo motor (1 1 ) are shown. MCU (7) and electronic control unit (8) are connected and communicate with each other for the system to work. The throttle (2) and brakes (5, 6) are connected to the ECU (8). Tightening rates of the throttle (2) and the brakes (5, 6) are gathered by the ECU (8). The rear brake handle (3) tightens the rear calliper and gives a brake-tightened rate thanks to the hall sensor as feedback. The feedback is used as input in the Al braking system. The motor control unit (7) is the unit that provides communication between the servo motor (11 ), and the ECU (8) and manages the servo motor (11 ). It returns servo motor (11 ) data including power consumption, velocity, etc to the ECU (8).

[0034] As shown in Figure 3, the data to train artificial intelligence model is collected in short periods while the experienced user is riding. Figure 4 shows that the artificial intelligence model is trained with the collected data and the trained models are distributed to all vehicles. To start using the invention, the user powers his 2-wheeled vehicle and presses the throttle (2). Thus the vehicle moves. If the vehicle needs to stop, the user applies the primary brakes (rear brake (5)) on the vehicle. Figure 5 shows that the model within the system receives inputs such as the degree of brake engagement, vehicle speed, accelerometer (9), and gyroscope (10) data. Based on these inputs, Al supported ECU (8) gives the brake-tightening decision, calculates the optimal amount of pressure to apply to the secondary / front brake (6) and how long it will be applied. This process occurs in rapid succession, with calculations being updated every few milliseconds. The outcomes of these calculations are then communicated to the ECU (8), which in turn sends commands to the motor (11 ). ECU (8) sends neutral signals and disables the throttle (2) when brakes. The motor (1 1 ) adjusts the brake calliper accordingly, ensuring that braking is performed efficiently and with the support of Al technology.

[0035] The presented braking method comprises following steps:

[0036] • applying a rear brake (5) on a vehicle by tightening a rear calliper by a rear brake handle (3) that gives the rear brake (5) tightening rate thanks to a hall sensor, • gathering an input data including acceleration on X, Y and Z axis, orientation, angular velocity, speed, and rear brake (5) tightening rate in an electronic control unit (8),

[0037] • making an artificial intelligent supported decision on applying a front brake (6) decision, how long the front brake (6) will be applied and how much the front brake (6) will be tightened when a user tightens the rear brake (5), according to the input data,

[0038] • managing a servo motor (1 1 ) that applies the brake decision by squeezing a front calliper via a motor control unit (7).

Claims

CLAIMS1. A braking system for two-wheeled vehicles, enhanced with artificial intelligence, comprising:• an accelerometer (9) measures acceleration on X, Y and Z axis,• a gyroscope (10) measures orientation and angular velocity,• a rear brake handle (3) tightens rear calliper and gives a rear brake (5) tightening rate thanks to a hall sensor,• an electronic control unit (8) makes an artificial intelligent supported decision on applying front brake (6), how long the front brake (6) will be applied and how much the front brake (6) will be tightened when a user applies rear brake (5) on the vehicle according to input data including acceleration, orientation, angular velocity, speed, and rear brake (5) tightening rate,• a motor control unit (7) determines and sends speed and power consumption data to the electronic control unit (8) and manages a servo motor (1 1 ) that applies the brake decision by squeezing a front calliper.

2. A braking method for two-wheeled vehicles, enhanced with artificial intelligence, comprising steps of:• applying a rear brake (5) on a vehicle by tightening a rear calliper by a rear brake handle (3) that gives the rear brake (5) tightening rate thanks to a hall sensor,• gathering an input data including acceleration on X, Y and Z axis, orientation, angular velocity, speed, and rear brake (5) tightening rate in an electronic control unit (8),• making an artificial intelligent supported decision on applying a front brake (6) decision, how long the front brake (6) will be applied and how much the front brake (6) will be tightened when a user tightens the rear brake (5), according to the input data,• managing a servo motor (11 ) that applies the brake decision by squeezing a front calliper via a motor control unit (7).

3. The braking method according to claim 2, wherein the input data is collected by:• measuring acceleration on X, Y and Z axis by an accelerometer (9),• measuring orientation and angular velocity via a gyroscope (10),• determining speed and power consumption data by means of the motor control unit (7).

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