Advanced Intelligent Signal System (AISS) Controlled Unsafe Driving and Overspeed Warning Indicators for Two Wheeler to Alert nearby Vehicles

Abstract

The present invention relates to an advanced safety system integrated with visual warning indicators for two-wheeler vehicles, designed to enhance the rider safety and to alert nearby vehicles by proactively addressing unsafe driving behaviours and overspeeding. The system includes a Helmet Integrated Sensor Module (HISM) capable of detecting helmet usage, alcohol consumption, drowsiness, and non-alertness through real-time monitoring of the rider's condition. A Vehicle Integrated Sensor Module (VISM) is positioned at critical points to capture data on single-hand driving, sudden acceleration, harsh braking, and mechanical vibrations. These sensors are connected to two separate Raspberry Pi units - one for the helmet sensors and one for the vehicle sensors - which communicate via Bluetooth and having Internet capability. The data is analyzed using advanced algorithms to detect unsafe driving instances, including single-hand driving, and generate alerts. Additionally, a GPS-enabled module determines the vehicle’s location and calculates appropriate speed limits for different road conditions. Based on this data, the system activates visual warning indicators, with a GREEN LED alerts overspeeding and a BLUE LED indicating unsafe driving. This dual Raspberry Pi configuration ensures wireless, real-time analysis and warning signals, offering a strong, proactive safety solution that aims to reduce accidents and enhance road safety for all vehicles and passengers.

Specification

Description:COMPLETE SPECIFICATION

The following specification particularly describes the invention and the manner in which it performs.

1. FIELD OF THE INVENTION

The present invention relates to a safety of two-wheeler vehicle rider and nearby vehicles from unsafe driving and overspeed driving through warning indicator signals.

2. BACKGROUND OF INVENTION

Today, two-wheelers are a popular choice for millions worldwide, offering convenience in transportation. However, their reckless riding brings life risks, especially considering their exposure in traffic and highways. Despite efforts with traditional safety measures like helmets and reflective gears, addressing dynamic challenges remains unsolved. These traditional safety measures for two-wheelers often fall short in overcoming unsafe driving situations proactively, leaving riders and nearby vehicles to the maximum possibilities of accident's risk. This highlights the greater need for innovative solutions capable of proactive risk identification to enhance overall safety on the roads. Before we introduce the present invention idea, let us enlist the present status of conventional safety provisions in two wheeler vehicles.

Present Situation

Traditional safety measures such as helmets and reflective gear have been used as two-wheeler safety devices. Helmets, in particular, serve as the primary line of security for riders, providing crucial protection for the head and brain in the event of a crash or collision. Properly fitted helmets have been proven to considerably reduce the risk of head injuries and fatalities in motorcycle accidents.

Reflective gear, including vests, jackets, and strips, enhances visibility, especially during low-light conditions at night time or cloudy weather. By reflecting light from headlights, these gear items make riders more visible, eye-catching to other road users, reducing the possibility of accidents caused by visibility issues.

While helmets and reflective gear play essential roles in reducing risks, they primarily operate as passive safety measures. They provide protection after an accident has occurred but do little to prevent accidents from happening in the first place. Moreover, their effectiveness can be limited in certain situations, such as high-speed crashes or collisions involving multiple vehicles.

Therefore, while helmets and reflective gear remain vital components of two-wheeler safety, there is a growing need for additional supportive technological arrangement that can proactively identify and address hazardous driving situations in real-time. Integrating advanced sensors, artificial intelligence based data analysis process, and communication systems into two-wheelers could revolutionize safety by enabling proactive risk detection and prevention, ultimately saving lives and reducing the incidence of accidents on the roads.

Problem Associated
The absence of proactive safety systems for two-wheelers increases possibility of occurrence of accidents caused by various unsafe and overspeed driving behaviours by rider on road. These all unsafe driving behaviours are listed below

[1]. One significant issue is the failure or casual attitude to wear helmet, which exposes riders to severe head injuries and fatalities in the event of a crash.
[2]. Instances of drunk driving or intoxicate significantly impair the rider's judgment, coordination, and reaction time, increasing the possibilities of accidents and injuries not only to them but also to nearby vehicles.
[3]. Driving longer period with single hand due to many reasons, especially talking on mobile phones during driving prone to accidents.
[4]. Drowsy riding presents another significant risk factor, particularly during long-distance travel or late-night rides.
[5]. Fatigue can spoil cognitive function and decrease alertness, leading to delayed reaction times and a higher probability of accidents.
[6]. Abnormal acceleration of two wheeler suddenly increases speed which results in unsafe driving, mechanical failure, and human error leads to distraction and losing the rider's control and leading to accidents.
[7]. Sudden braking or erratic lane changes, often due to negligence or misjudgement, can become hindrance to nearby vehicles.
[8]. Most significant risk is overspeeding in which driver was unable to react in time to unexpected road conditions or obstacles, leading to a collision and responsible for the large accident. Speed above 80 km/hr is considered as overspeed driving.

These unsafe driving behaviours not only put in danger the lives of two-wheeler riders but also create a significant threat to nearby vehicles and pedestrians. In the event of an accident, the impact can be severe, resulting in serious injuries or fatalities for all parties involved. Moreover, accidents caused by two-wheeler riders can lead to traffic congestion, property damage, and emotional shock for affected individuals and communities.

Addressing these safety concerns requires a comprehensive approach that goes beyond traditional measures and incorporates proactive safety systems capable of identifying and providing technologies and intelligent systems, such as sensors, algorithms incorporated in Raspberry Pi, communication through vehicle indicators; it is possible to create a safer driving environment for two-wheeler riders, nearby vehicles and other road users. Ultimately it will reduce the incidence of accidents and promoting safer roads for everyone.

Features of Present Invention
The proposed invention of Advanced Intelligent Signal System (AISS) controlled warning Indicators addresses these challenges by incorporating advanced sensors and signal analysis techniques to detect unsafe driving behaviours and overspeeding in real-time to alert nearby vehicles. Key features of the invention include:

[1] Raspberry Pi helps us to connect the variety of sensors from HISM (Helmet Integrated Sensor Module) and VISM (Vehicle Integrated Sensor Module) and adds intelligence to the connection between Helmet mounted sensors and Vehicle mounted sensors. Rasberry Pi is small, portable, low-cost, credit-card sized Single-Board Computers (SBCs) features System-on-Chips (SoC) with a dedicated ARM (Advanced RISC Machines) compatible Central Processing Unit (CPU), RAM(Random Access Memory). It runs on Raspbian OS (Operating System) which is a modified Linux based light-weight OS. It supports various connectivity options such as Bluetooth, Wi-Fi (Wireless Fidelity) and Ethernet.
[2] Raspberry Pi supports Python 3.12 programming language. All sensors mounted on HISM and VISM are connected with Raspberry Pi-1 and Raspberry Pi-2 and programmed using Python.
[3] Integration of different sensors positioned on the helmet and various locations in the two-wheeler to collect data on mentioned unsafe and overspeed driving parameters.
[4] Utilization of advanced algorithms using Raspberry Pi to process signal data and identify instances of unsafe and overspeed driving behaviours.
[5] Incorporation of GPS technology to determine the vehicle's position and calculate appropriate speed limits for different road conditions to generate overspeed driving signal.
[6] Activation of visual warning indicators on the front end and rear end of the two-wheeler to alert both the rider, nearby vehicles and other road users of potential danger.

Justification of Invention:
The Advanced Intelligent Signal System (AISS) Controlled Unsafe Driving and Overspeed warning Indicators for Two wheeler to Alert nearby Vehicles, fills a critical gap in two-wheeler safety technology by offering a proactive solution to address unsafe driving behaviours and overspeeding. By leveraging intelligent signal systems and real-time data analysis, the invention can effectively identify and reduce potential risks by creating alert signal, thereby enhancing the safety of riders and other road users. Thus it is really beneficial to solve road safety issues of society. At present, such proactive safety technology for two wheelers is not available.

3. SUMMARY OF THE INVENTION

The present disclosure is about Advanced Intelligent Signal System (AISS) controlled Unsafe Driving and Overspeed Warning Indicators to Alert nearby Vehicles. This disclosure is innovative solution designed to enhance the safety of two-wheeler riders and nearby vehicles by proactively addressing unsafe driving behaviours and overspeeding activities. The unsafe behaviour includes usage of helmet, alcohol drunk, drowsiness in driving, non alertness, and single hand driving for long period etc. The present invention identifies such situation or behaviours using Raspberry Pi algorithm and created warning signal in indicators for nearby vehicles. The invention is comprised by the following five embodiments

1) Helmet Integrated Sensor Module (HISM)
In this embodiment of present disclosure, sensors are integrated directly into the helmet worn by the rider. These sensors are strategically positioned to detect vital parameters such as helmet usage, head and eye movements indicating drowsiness, and non alertness during driving. The data collected from these sensors is transmitted wirelessly to the Helmet Raspberry Pi-1 control unit of the AISS installed on the two-wheeler.

2) Vehicle Integrated Sensor Module (VISM)
In this embodiment of present disclosure, a setup of four different sensors along with Raspberry Pi-2 is mounted on the two-wheeler vehicle. These sensors are placed at key locations such as the handlebars, seat, and engine to capture data on holding of handle by hands, acceleration, braking, and mechanical performance. Additionally, sensors may also be embedded in the vehicle's frame to detect vibration. These sensors are used to detect erratic and overspeeding instances.

3) Advanced Algorithmic Analysis
The present invention focuses on the software aspect of the AISS using two separate Raspberry Pi for helmet and vehicle integrated sensor modules for sensors signal data analysis. These two Raspberry Pi devices communicate each other wirelessly through Bluetooth connectivity to ensure to identify the event triggered, and generate the appropriate response. Advanced algorithms are developed and implemented to process the data collected from the sensors in real-time. These algorithms utilize coding to analyze desire limits and identify instances of unsafe driving behaviours such as helmet usage, alcohol impairment, drowsiness, sudden acceleration, harsh braking, and turn sharply.

4) GPS-Enabled Speed Limit Calculation Module
The present invention describes the embodiment comprises of GPS technology which is integrated into the AISS to determine the vehicle's position and decide predefined speed limits for different locations such as city, highways, rural areas etc. The GPS system continuously monitors the vehicle's location and compares it to a database of speed limits for various road and locations. If the vehicle exceeds the designated speed limit for a particular area or road, then AISS triggers visual warning indicators to alert the rider and nearby vehicles.

5) Visual Warning Indicator Activation
This embodiment of invention focuses on the implementation of the visual warning indicators themselves. High-intensity coloured LED light indicators are mounted on the front and rear sides of the two-wheeler vehicle. The GREEN coloured LED indicator is used for overspeeding and BLUE coloured LED indicator is used for unsafe driving indications. When the AISS detects unsafe driving behaviours or overspeeding, it activates these indicators to emit bright flashing lights, effectively alerting the rider, nearby vehicles, and pedestrians of potential danger. The activation of the indicators is synchronized with the severity of the detected behaviour, ensuring timely and appropriate warnings.

The AISS controlled warning indicators invention fill a critical gap in two-wheeler safety technology by providing a proactive solution to mitigate risks and promote safer roads for everyone.

The earlier is a simplified summary to provide an understanding of some embodiments of the present disclosure. This summary is neither an extensive nor exhaustive overview of the present disclosure and its various embodiments. It doesn't cover everything, but it helps to understand the main concepts. There are more details explained below. Also, there could be other ways to use or combine the features mentioned earlier to make different versions of this invention, as will be appreciated.

4. BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1. Different Sensors used in Helmet Integrated Sensors Module
It shows the location of different sensors used in the Helmet Integrated Sensor Module. The part list shown in the Figure 1 is like this
1. Infrared (IR) proximity sensor for Helmet detection, 2. Flexes sensing (Pressure Sensor) fitted in chin strap for Helmet position, 3. Pressure Sensor fitted in Helmet strap buckle to ensure strap fastening, 4. Position Sensor used to detect Helmet orientation and angular Position, 5. Alcohol Consumption Sensor to detect alcohol presence, 6. Head Movement Sensor, 7. Eye Flicker Sensor mounted on the face shield of helmet, 12. Face Shield of Helmet 13. Chin Strap, 14. Rigid Outer Metallic Shell
Figure 2. Different Sensors used in Vehicle-Integrated Sensors Module
It shows location of different sensors used in the Vehicle Integrated Sensor Module
The part list shown in the Figure 2 is like this
8. Acceleration Sensor mounted near the front wheel axle 9. Braking Sensor located near the brake pad system 10. Vibration Sensor installed on the vehicle engine's frame 11. Pressure Sensors fitted in the bike handle grip 15. Rear BLUE colored unsafe driving indicator 16. Rear GREEN colored overspeed driving indicator 17. Front BLUE colored Unsafe driving Indicator 18. Front GREEN colored overspeed Driving Indicator 19. Tail Brake Light 20. Front Head light 21. Brake Drum of back wheel 22. Speedometer of two wheelers.
Figure 3. Unsafe and Over Speed Warning Rear Indicators
This Figure 3 shows the design of warning indicators for unsafe and overspeed driving. These indicators provide visual (dark BLUE and dark GREEN indicators) and auditory alerts to nearby vehicles and the rider, enhancing safety by drawing attention to hazardous driving conditions. The part list shown in the Figure 3 is like this
15. Dark BLUE coloured indicator for unsafe driving 16. Dark GREEN coloured indicator for overspeeding 23. Auto beep sound horn on overspeeding

Figure 4: Bike rider's Androids hotspot is connected to Raspberry Pi -1 which is mounted on Helmet Integrated Sensors Module (HISM) and another Raspberry Pi -2 mounted on Vehicle Integrated Sensor Module (VISM) through Bluetooth


Figure 5: Data Flow Diagram of Level - 0 for Helmet Integrated Sensors Module (HISM)
It illustrates the flow of signal data generated by various sensors within the module. It outlines the sequence of data processing steps required to generate a warning signal through an indicator in the event of an unsafe driving incident.
Figure 6: Data Flow Diagram of Level - 0 for Vehicle Integrated and Helmet Integrated Sensors Module
It illustrates the flow of signal data generated by the Vehicle Integrated Module in conjunction with the Helmet Integrated Sensor Module. It shows the process of creating unsafe driving and overspeed warning alerts for nearby vehicles and the two-wheeler rider. The diagram outlines the primary and dominant steps in the sequence, from data acquisition by sensors, data analysis by the Raspberry Pi, to the generation of alerts by the unsafe driving and overspeed indicators.
Figure 7: Data Flow Diagram of Level - 2 for Helmet Integrated Sensor Module
It is the Level 2 Data Flow Diagram (DFD) for the Helmet Integrated Sensor Module. It details the flow and processing of data for detecting unsafe driving conditions, leading to the generation of warning alerts. It includes six main processes: data acquisition by sensors, data transmission to a central processor (Raspberry Pi-1), data processing and analysis, warning signal generation, and alert transmission to the rider and nearby vehicles. This systematic approach ensures rider safety by providing continuous monitoring and timely warnings.
Figure 8: Data Flow Diagram of Level - 2 for Vehicle Integrated Sensor Module
It illustrates DFD level -2 for Vehicle Integrated Sensor Module which shows a detailed breakdown of the data flow from sensors mounted on vehicle and processing steps used to identify unsafe and overspeed driving conditions and generate appropriate warning alerts. It includes captures data from vehicle mounted sensors, processes it via a Raspberry Pi - 2, and generates visual (dark BLUE and dark GREEN indicators) and audio (beep sound horn) alerts.
Figure 9: It shows flowchart which illustrates the operation of a Helmet Integrated Sensor Module, detailing its continuous monitoring of rider safety parameters and communication with the Vehicle Raspberry Pi via Bluetooth to send either a "SAFE" or "UNSAFE" status to the Vehicle Raspberry Pi based on the sensor readings.

5. DETAILED DESCRIPTION OF THE INVENTION

In the present disclosure, the embodiment of the Helmet-Integrated Sensor Module is comprised of seven different sensors, which are used to detect unsafe driving behaviours related to helmet usage, drunk driving, head movements, and abnormal eye-flicker indicating drowsiness and non-alertness during driving.

1. Helmet Detection Sensors (1 and 2)
The helmet-worn detection sensor utilizes infrared (IR) proximity sensing and flex sensing (Pressure Sensor) technology. The IR sensor (1) detects the proximity of the helmet, while the flex sensor (2) detects it's positioning. This combination ensures accurate detection of helmet presence and its position.
[1] Sensor -1 used in module: Sharp GP2Y0A21YK0F IR Proximity Sensor
[2] Location of Sensor: Front of the helmet
[3] Threshold Values: Distance < 5 cm indicates helmet worn
[1] Sensor -2 used in module: Flex Sensor 2.2"
[2] Location of Sensor: Inside the chin strap
[3] Threshold Values: Bend angle > 45° indicates correct positioning
2. Helmet Usage Sensor (Pressure Sensor, 3)
This sensor measures the pressure exerted on the chin strap or buckle of the helmet. When properly worn, the helmet creates a specific pressure on the strap. If the pressure drops below a certain threshold, it indicates that the helmet may not be securely fastened or worn correctly.
[1] Sensor -3 used in module:HX711 Pressure Sensor
[2] Location of Sensor: Chin strap buckle
[3] Threshold Values: Pressure < 5 N indicates insecure fastening
3. Position Sensor (4)
This sensor checks whether the rider has worn the helmet in the appropriate orientation (i.e., angular position) or not. In the present disclosure, the LM393 Position Sensor is used for this purpose.
[1] Sensor -4 used in module: LM393 Tilt Sensor
[2] Location of Sensor: Top center of the helmet
[3] Threshold Values: Tilt angle > 15° indicates incorrect orientation
4. Alcohol Consumption Sensor (5)
This sensor employs a semiconductor-based alcohol sensor or fuel cell sensor to detect the presence of alcohol vapors in the rider's breath. In the present invention, the MQ3 Gas Sensor is used to sense the presence of alcohol in the rider's breath.
[1] Sensor -5 used in module: MQ3 Alcohol Sensor
[2] Location of Sensor: Near the helmet's mouth area
[3] Threshold Values: Alcohol concentration > 0.08% BAC
5. Head Movement Sensor (6)
This sensor utilizes either an accelerometer or a gyroscope to detect changes in the helmet's orientation and angular velocity. It measures the rate of rotation and acceleration experienced by the rider's head. Sudden or erratic movements beyond predetermined thresholds may indicate drowsiness or non-alertness during driving. The ADXL335 Accelerometer Module is used.
[1] Sensor -6 used in module: ADXL335 Accelerometer
[2] Location of Sensor: Inside the helmet near the top
[3] Threshold Values: Acceleration > 2g indicates erratic movements
6. Eye Flicker Sensor (7)
This compact sensor is designed to detect rapid movements or flickers of the eye. It operates based on optical sensing principles, capturing subtle changes in light reflection caused by eye movements. The RC-A-41575 Eye Flicker Sensor is used in the present invention to detect drowsiness or non-alertness during driving.
[1] Sensor -7 used in module: RC-A-41575 Eye Flicker Sensor
[2] Location of Sensor: Mounted on the face shield of the helmet
[3] Threshold Values: Flicker rate < 10 flickers/minute indicates drowsiness
The second embodiment of the present disclosure is the Vehicle-Integrated Sensor Module, which is comprised of various sensors positioned at key locations on the two-wheeler to monitor its performance and behaviour. These sensors include:

1. Acceleration Sensor (8)
Placed near the front wheel axle close to the speedometer sensor or on the frame, this sensor measures changes in velocity to detect sudden acceleration or deceleration.
[1] Sensor -8 used in module: MPU6050 Accelerometer
[2] Location of Sensor: Near the front wheel axle
[3] Threshold Values: Acceleration > 5 m/s² indicates sudden acceleration
2. Braking Sensor (9)
This pressure sensor is located near the brake pad and detects the application of brakes, measuring the intensity of braking force application.
[1] Sensor -9 used in module: BMP180 Pressure Sensor
[2] Location of Sensor: Near the brake pad
[3] Threshold Values: Pressure > 50 N indicates intense braking
3. Vibration Sensor (10)
Installed on the vehicle's frame, this sensor detects vibrations caused by irregularities in the road surface or mechanical issues.
[1] Sensor -10 used in module: SW-420 Vibration Sensor
[2] Location of Sensor: On the vehicle frame/ Nearer to engine
[3] Threshold Values: Vibration intensity > 10 Hz indicates mechanical issues
4. Pressure Sensor (11)
Fitted in the handle grip, this sensor ensures that the rider drives the bike by holding both hands. When the rider drives the bike with a single hand for a prolonged period, this sensor creates a signal to indicate a casual driving incident.
[1] Sensor -11 used in module: FSR402 Force Sensing Resistor
[2] Location of Sensor: Inside the handle grip
[3] Threshold Values: Pressure < 1 N indicates single hand driving
Advanced Algorithmic Analysis
Advanced algorithms in vehicle and helmet safety systems utilize real-time sensor data to detect unsafe driving behaviors and overspeeding. These algorithms continuously monitor and analyze inputs from various sensors, applying machine learning techniques to identify indicative of risky behaviors. By comparing the sensor readings to pre-set threshold values from sensors such as Sensor 1 (IR Proximity), Sensor 2 (Flex), Sensor 3 (Pressure), Sensor 4 (Tilt), Sensor 5 (Alcohol), Sensor 6 (Accelerometer), and Sensor 7 (Eye Flicker), the system can detect conditions like improper helmet usage, drowsiness, and alcohol influence etc. For the vehicle, data from Sensor 8 (MPU6050 Accelerometer and Gyroscope), Sensor 9 (BMP180 Pressure Sensor), Sensor 10 (SW-420 Vibration Sensor), and Sensor 11 (FSR402 Force Sensing Resistor) are processed to detect unsafe driving conditions such as excessive acceleration, intense braking, mechanical issues, or improper hand grip.
When these risky conditions are identified, Bluetooth communication between Raspberry Pi-1 (Helmet Pi) and Raspberry Pi-2 (Vehicle Pi) ensures that signals are transmitted. If any threshold is crossed, such as the helmet being improperly worn or the vehicle exceeding the speed limit based on GPS data from Sensor 24 (GPS Module), LED indicators (15,16) are triggered to alert the rider in real time. This coordinated system provides immediate feedback to enhance rider safety and maintain regular driving attention.
The advanced algorithm for these two systems is designed as given below, integrating Helmet Raspberry Pi and Vehicle Raspberry Pi for coordinated safety operations. These algorithms ensure efficient detection and response to unsafe conditions by maintaining constant communication between the Helmet and Vehicle Pi systems, utilizing Bluetooth for wireless data exchange.
Advanced Algorithm for Helmet Integrated Sensor Module Python Programming Code
1. Initialize System: Set up GPIO pins, libraries, and Bluetooth communication.
2. Configure Sensors: Set up IR Proximity, Flex, HX711 Pressure, LM393 Tilt, MQ3 Alcohol, ADXL335 Accelerometer, and Eye Flicker Sensors.
3. Read Sensor Data: Continuously monitor data from all sensors.
4. Process Data: Apply machine learning to detect unsafe helmet conditions (e.g., improper helmet use or alcohol presence etc.).
5. Generate and Send Alerts: Transmit alerts via Bluetooth to Vehicle Raspberry Pi if unsafe conditions are detected.
6. Repeat Loop: Continuously update and process sensor data.
Refer the Figure 5 and 7of DFD Level - 0 and 2 of Helmet Integrated Sensor Module respectively
Advanced Algorithm for Vehicle Integrated Sensor Module Python Programming Code
1. Initialize System: Set up GPIO pins, libraries, and Bluetooth communication.
2. Configure Sensors: Set up MPU6050 Accelerometer, BMP180 Pressure Sensor, SW-420 Vibration Sensor, FSR402 Grip Sensor, and GPS.
3. Read Sensor Data: Continuously monitor data from all sensors.
4. Process Data: Use machine learning to analyze driving behaviour (e.g., acceleration, braking pressure, and vibration) and compare speed with limits.
5. Check Helmet Data: Receive and process data from Helmet Pi for unsafe helmet usage.
6. Activate Indicators: Trigger LEDs based on unsafe conditions (e.g., helmet issues or overspeeding).
7. Repeat Loop: Continuously monitor vehicle performance and safety.
Refer the Figure 6 and 8 of DFD Level - 0 and 2 of Vehicle Integrated Sensor Module respectively
Algorithm for GPS Location Identification and Speed Limit Detection
1. Initialize GPS: Set up the GPS module for location and speed data.
2. Obtain Location: Continuously read GPS coordinates.
3. Determine Speed Limit: Query speed limits based on current location.
4. Compare Speed: Compare vehicle speed with the speed limit.
5. Detect Over speed: Flag overspeeding and signal Vehicle Raspberry Pi to activate the overspeed indicator.
6. Repeat Loop: Continuously update location and speed.
Communication between Helmet Raspberry Pi-1 and Vehicle Raspberry Pi-2 Through Bluetooth
1. Initialize Bluetooth: Set up Bluetooth on both Raspberry Pi devices.
2. Send Data: Raspberry Pi-1 transmits helmet data to Raspberry Pi-2.
3. Receive Data: Raspberry Pi-2 receives and processes helmet data.
4. Activate Alerts: Raspberry Pi-2 triggers indicators based on received data.
5. Maintain Communication: Ensure continuous data exchange for real-time updates.
Python Coding for Helmet Integrated Sensor System (Helmet Raspberry Pi Code) and Vehicle Integrated Sensor System (Vehicle Raspberry Pi Code)
By utilizing the two advanced algorithms for Helmet Raspberry Pi -1 and Vehicle Raspberry Pi -2 systems, separate Python code implementations have been developed. The comments included in the code provide a clear understanding of the system's workflow and the interaction between different components. These explanations help clarify how each sensor and module functions within the overall architecture.
In Helmet Raspberry Pi -1, sensor readings are processed continuously to detect unsafe conditions, such as improper helmet use or alcohol detection etc. For each section of the code, comments and work flow give general guideline about the logic of helmet safety monitoring, highlighting how data is gathered and transmitted to Vehicle Raspberry Pi -2 via Bluetooth.
Similarly, the Vehicle Raspberry Pi -2 code is designed to process data from multiple vehicle sensors, including acceleration, braking, vibration, and GPS modules. The commented code helps illustrate how the system identifies unsafe driving behaviors, overspeeding or single-handed driving, and triggers LED (GREEN LED for Over Speed and Blue LED for Unsafe Driving ) indicators accordingly. By leveraging real-time Bluetooth communication, Vehicle Raspberry Pi -2 also receives data from Helmet Raspberry Pi -1 and responds to rider safety concerns.
Together, these two programs work in unison to enhance overall rider safety, and the comments in the code serve as an essential guide for understanding their interconnected functionalities.
Python Coding for Helmet Integrated Sensor System (Helmet Raspberry Pi -1 Code)
import RPi.GPIO as GPIO
from time import sleep
import bluetooth

# Sensor Thresholds
HELMET_WORN_THRESHOLD = 5 # IR Proximity Sensor (1), distance in cm
HELMET_POSITION_THRESHOLD = 45 # Flex Sensor (2), bend angle in degrees
HELMET_PRESSURE_THRESHOLD = 5 # Pressure Sensor (3), pressure in N
HELMET_TILT_THRESHOLD = 15 # Tilt Sensor (4), angle in degrees
ALCOHOL_THRESHOLD = 0.08 # Alcohol Sensor (5), BAC percentage
HEAD_MOVEMENT_THRESHOLD = 2 # Accelerometer (6), acceleration in g
EYE_FLICKER_THRESHOLD = 10 # Eye Flicker Sensor (7), flickers per minute

# Bluetooth communication setup
VEHICLE_PI_MAC = 'XX:XX:XX:XX:XX:XX' # MAC address of Vehicle Pi Bluetooth
bluetooth_socket = bluetooth.BluetoothSocket(bluetooth.RFCOMM)

def connect_to_vehicle_pi():
"""Establish Bluetooth connection to Vehicle Pi"""
try:
bluetooth_socket.connect((VEHICLE_PI_MAC, 1))
print("Connected to Vehicle Pi")
except bluetooth.btcommon.BluetoothError as err:
print(f"Bluetooth connection failed: {err}")

def check_helmet_worn():
"""Check if helmet is worn using IR Proximity Sensor (1)"""
# Simulate sensor reading
proximity = 4 # Example value, replace with actual sensor reading
return proximity < HELMET_WORN_THRESHOLD

def check_helmet_position():
"""Check helmet position using Flex Sensor (2)"""
# Simulate sensor reading
bend_angle = 50 # Example value, replace with actual sensor reading
return bend_angle > HELMET_POSITION_THRESHOLD

def check_helmet_pressure():
"""Check helmet pressure using Pressure Sensor (3)"""
# Simulate sensor reading
pressure = 6 # Example value, replace with actual sensor reading
return pressure > HELMET_PRESSURE_THRESHOLD

def check_helmet_tilt():
"""Check helmet tilt using Tilt Sensor (4)"""
# Simulate sensor reading
tilt_angle = 20 # Example value, replace with actual sensor reading
return tilt_angle > HELMET_TILT_THRESHOLD

def check_alcohol_level():
"""Check alcohol level using Alcohol Sensor (5)"""
# Simulate sensor reading
alcohol_level = 0.09 # Example value, replace with actual sensor reading
return alcohol_level > ALCOHOL_THRESHOLD

def check_head_movement():
"""Check head movement using Accelerometer (6)"""
# Simulate sensor reading
acceleration = 2.5 # Example value, replace with actual sensor reading
return acceleration > HEAD_MOVEMENT_THRESHOLD

def check_eye_flicker():
"""Check eye flicker rate using Eye Flicker Sensor (7)"""
# Simulate sensor reading
flicker_rate = 8 # Example value, replace with actual sensor reading
return flicker_rate < EYE_FLICKER_THRESHOLD

def process_helmet_data():
"""Process helmet data and determine safety status"""
helmet_worn = check_helmet_worn()
helmet_position_correct = check_helmet_position()
helmet_pressure_ok = check_helmet_pressure()
helmet_tilt_ok = not check_helmet_tilt()
alcohol_detected = check_alcohol_level()
head_movement_erratic = check_head_movement()
drowsiness_detected = check_eye_flicker()

unsafe_driving = not (helmet_worn and helmet_position_correct and helmet_pressure_ok and helmet_tilt_ok and not alcohol_detected and not head_movement_erratic and not drowsiness_detected)

return "UNSAFE" if unsafe_driving else "SAFE"

def send_data_to_vehicle_pi(safety_status):
"""Send safety status to Vehicle Pi"""
bluetooth_socket.send(safety_status)

def main():
"""Main function to run the helmet module"""
connect_to_vehicle_pi()

while True:
safety_status = process_helmet_data()
send_data_to_vehicle_pi(safety_status)
sleep(1) # Delay before next check

if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
bluetooth_socket.close()
Detailed Explanation of the Helmet Integrated Sensor System Code (Helmet Raspberry Pi -1 Code)
The Helmet Raspberry Pi -1 Code is designed to assess the safety of a helmet-wearer using a range of sensors and communicate the safety status to a vehicle's onboard system via Bluetooth. Initially, the code imports necessary libraries such as `RPi.GPIO` for GPIO pin control, `time.sleep` for implementing delays, and `bluetooth` for Bluetooth communication. It then defines threshold values for each sensor used, which help determine if conditions are safe or unsafe.
The code sets up Bluetooth communication by creating a Bluetooth socket and specifying the Vehicle Raspberry Pi's MAC address. The `connect_to_vehicle_pi()` function establishes this connection, handling any connection errors that might arise. Refer Figure 4.
After setting up the environment, the program enters a continuous loop where it reads data from each of the helmet's sensors. The IR Proximity Sensor detects whether the helmet is being worn by measuring the distance between the sensor and the helmet. If the distance is less than 5 cm, it confirms that the helmet is worn properly. The Flex Sensor assesses the angle of the helmet's bend to ensure it is correctly positioned. If the bend angle exceeds 45 degrees, it may indicate improper positioning of the helmet. The Pressure Sensor monitors the pressure applied by the chin strap, ensuring it is securely fastened. A pressure reading below 5 N signals that the strap might be loose or improperly fastened.
The Tilt Sensor checks the helmet's orientation, raising a flag if the tilt exceeds 15 degrees, suggesting that the helmet might be tilted too far to one side. The Alcohol Sensor measures the rider's breath alcohol concentration, flagging a risk if the level exceeds 0.08% BAC, which indicates potential alcohol influence. The Accelerometer tracks head movements, detecting erratic behavior if the acceleration exceeds 2g, signaling dangerous or sudden movements. The Eye Flicker Sensor monitors the rider's eye flicker rate to detect drowsiness, flagging it as a risk if the flicker rate drops below 10 flickers per minute.
The `process_helmet_data()` function integrates the results from these sensors to determine the overall safety status of the helmet-wearer. It assesses each condition against the defined thresholds and returns either "UNSAFE" or "SAFE" based on the combined results. This safety status is then communicated to the Vehicle Raspberry Pi-2 using the `send_data_to_vehicle_pi()` function, which transmits the status over Bluetooth.
In the main loop of the code, the `main()` function coordinates these tasks. It first establishes a Bluetooth connection to the Vehicle Pi and then continuously monitors the helmet data, sending updates on the safety status at regular intervals. This ensures that the helmet's safety status is consistently checked and communicated. The code includes exception handling to close the Bluetooth connection properly if the script is manually interrupted. This setup allows for real-time monitoring and timely responses based on the communicated safety status.
Python Coding for Vehicle Integrated Sensor System (Vehicle Raspberry Pi -2 Code)

import RPi.GPIO as GPIO
from time import sleep
import bluetooth
import serial

# Sensor Thresholds
ACCELERATION_THRESHOLD = 5 # Accelerometer and Gyroscope (8), acceleration in m/s²
BRAKING_PRESSURE_THRESHOLD = 50 # Pressure Sensor (9), force in N
VIBRATION_THRESHOLD = 10 # Vibration Sensor (10), intensity in Hz
SINGLE_HAND_DRIVING_THRESHOLD = 1 # Pressure Sensor (11), pressure in N
OVER_SPEED_THRESHOLD = 60 # Speed limit in km/h

# LED Indicators
GREEN_LED_PIN = 17 # Over-speeding indicator
BLUE_LED_PIN = 27 # Unsafe driving indicator

# Bluetooth communication setup
HELMET_PI_MAC = 'XX:XX:XX:XX:XX:XX' # MAC address of Helmet Pi Bluetooth
bluetooth_socket = bluetooth.BluetoothSocket(bluetooth.RFCOMM)

# GPS and Serial setup
ser = serial.Serial('/dev/ttyAMA0', 9600, timeout=1)

# Setup GPIO pins
GPIO.setmode(GPIO.BCM)
GPIO.setup(GREEN_LED_PIN, GPIO.OUT)
GPIO.setup(BLUE_LED_PIN, GPIO.OUT)

def connect_to_helmet_pi():
"""Establish Bluetooth connection to Helmet Pi"""
try:
bluetooth_socket.connect((HELMET_PI_MAC, 1))
print("Connected to Helmet Pi")
except bluetooth.btcommon.BluetoothError as err:
print(f"Bluetooth connection failed: {err}")

def get_gps_location_and_speed():
"""Retrieve GPS location and speed from GPS module"""
gps_data = ser.readline().decode('utf-8') # Read and decode GPS data
speed = int(gps_data.split(",")[7]) # Assume speed is in km/h and is the 8th value
return speed

def check_acceleration():
"""Check vehicle acceleration using Accelerometer and Gyroscope (8)"""
# Simulate sensor reading
acceleration = 6 # Example value, replace with actual sensor reading
return acceleration > ACCELERATION_THRESHOLD

def check_braking_pressure():
"""Check braking pressure using Pressure Sensor (9)"""
# Simulate sensor reading
pressure = 60 # Example value, replace with actual sensor reading
return pressure > BRAKING_PRESSURE_THRESHOLD

def check_vibration():
"""Check vehicle vibration using Vibration Sensor (10)"""
# Simulate sensor reading
vibration_intensity = 15 # Example value, replace with actual sensor reading
return vibration_intensity > VIBRATION_THRESHOLD

def check_single_hand_driving():
"""Check single-hand driving using Pressure Sensor (11)"""
# Simulate sensor reading
pressure = 0.5 # Example value, replace with actual sensor reading
return pressure < SINGLE_HAND_DRIVING_THRESHOLD

def process_vehicle_data():
"""Process vehicle data and make decisions"""
over_speed = get_gps_location_and_speed() > OVER_SPEED_THRESHOLD
rapid_acceleration = check_acceleration()
intense_braking = check_braking_pressure()
excessive_vibration = check_vibration()
single_hand_driving = check_single_hand_driving()

return over_speed, rapid_acceleration, intense_braking, excessive_vibration, single_hand_driving
def receive_data_from_helmet_pi():
"""Receive safety status from Helmet Pi"""
data = bluetooth_socket.recv(1024).decode('utf-8')
return data == 'UNSAFE'

def main():
"""Main function to run the vehicle module"""
connect_to_helmet_pi()

while True:
over_speed, rapid_acceleration, intense_braking, excessive_vibration, single_hand_driving = process_vehicle_data()
unsafe_driving = receive_data_from_helmet_pi()

if over_speed:
GPIO.output(GREEN_LED_PIN, GPIO.HIGH) # Activate over-speeding indicator
else:
GPIO.output(GREEN_LED_PIN, GPIO.LOW) # Deactivate over-speeding indicator

if unsafe_driving or single_hand_driving:
GPIO.output(BLUE_LED_PIN, GPIO.HIGH) # Activate unsafe driving indicator
else:
GPIO.output(BLUE_LED_PIN, GPIO.LOW) # Deactivate unsafe driving indicator

sleep(1) # Delay before next check

if __name__ == "__main__":
try:
main()
except KeyboardInterrupt:
GPIO.cleanup()
bluetooth_socket.close()
Detailed Explanation of the Vehicle Integrated Sensor SystemCode (Vehicle Raspberry Pi -2 Code)
The Vehicle Raspberry Pi -2 Code is designed to monitor various vehicle parameters and interact with the helmet system to ensure overall driving safety. The code begins by importing the necessary libraries: RPi.GPIO for controlling GPIO pins, time.sleep for creating delays, bluetooth for Bluetooth communication, and serial for GPS data retrieval. It then sets up threshold values for various sensors that monitor vehicle behavior.
The program starts by initializing GPIO pins for indicators and setting up Bluetooth communication with the Helmet Raspberry Pi -1. It establishes a connection using the Helmet Pi's MAC address and prepares for data exchange.
The connect_to_helmet_pi() function handles the Bluetooth connection setup. It attempts to connect to the Helmet Raspberry Pi -1 and prints a success or error message based on the connection outcome. This function is crucial for enabling communication between the two Raspberry Pis.
For GPS location and speed detection, the get_gps_location_and_speed() function retrieves data from a GPS module connected via a serial interface. It reads the GPS data, which includes speed information, and extracts the speed in kilometers per hour. This speed is then compared against a predefined threshold to determine if the vehicle is exceeding the speed limit.
The code continuously checks several vehicle parameters through dedicated functions. The check_acceleration() function monitors vehicle acceleration using an accelerometer and gyroscope. It flags rapid acceleration if the reading exceeds 5 m/s². The check_braking_pressure() function ensures proper braking by checking pressure values; if the pressure exceeds 50 N, it indicates proper braking. The check_vibration() function assesses vehicle vibration, and if the intensity surpasses 10 Hz, it flags excessive vibration. The check_single_hand_driving() function checks if the vehicle is being driven with one hand by monitoring pressure readings; a pressure below 1 N suggests improper driving behavior.
The process_vehicle_data() function integrates data from these sensors to evaluate overall driving conditions. It identifies if the vehicle is over-speeding, accelerating rapidly, braking excessively, experiencing high vibration, or being driven with one hand.
The receive_data_from_helmet_pi() function receives the safety status from the Helmet Raspberry Pi -1 via Bluetooth. It decodes the received data to determine if the helmet-wearer and other rider behaviours are deemed unsafe.
In the main() function, the code sets up the connection to the Helmet Raspberry Pi - 1 and enters a continuous loop where it checks vehicle data, receives helmet status, and controls indicator LEDs based on the processed data. The GREEN LED is activated if the vehicle is over-speeding, while the BLUE LED's status is managed based on the helmet's safety status and single-hand driving condition.
Finally, the code handles any interruptions, ensuring that GPIO pins and Bluetooth connections are properly cleaned up to prevent resource leaks.
GPS Location Identification and Speed Limit Detection
GPS Location IdentificationandSpeed Limit Detectionare handled by a GPS module connected to the Vehicle Pi via a serial interface. The GPS module continuously provides location data, which includes the vehicle's speed in kilometers per hour (km/h). The get_gps_location_and_speed() function reads this serial data to extract the speed information.
To identify the speed limit, the function compares the extracted speed against a predefined threshold value. This threshold represents the maximum permissible speed for the current driving conditions or area. If the vehicle's speed exceeds this threshold, it is flagged as over-speeding. This comparison ensures that any dangerous driving behavior due to excessive speed is detected and can be acted upon, such as by activating relevant indicators or sending alerts.
Communication between Helmet Raspberry Pi-1 and Vehicle Raspberry Pi-2
Communicationbetween Helmet Raspberry Pi-1 (Helmet Pi) and Vehicle Raspberry Pi-2 (Vehicle Pi) is established using Bluetooth. Each Pi has its Bluetooth module configured to send and receive data. The Helmet Pi continuously monitors helmet-related safety parameters and sends its safety status ("UNSAFE" or "SAFE") to the Vehicle Raspberry Pi -2 over Bluetooth.
On the Vehicle Raspberry Pi -2 side, the receive_data_from_helmet_pi() function analyzes for incoming Bluetooth data from the Helmet Pi. It decodes the received message to determine the helmet's safety status. Based on this status and other vehicle data, the Vehicle Raspberry Pi -2 makes decisions about activating or deactivating indicators as well as managing other safety-related functionalities.
This communication enables real-time updates of the rider's safety status, which is critical for integrated safety systems. The Vehicle Raspberry Pi -2 uses this information to monitor not only vehicle conditions but also the helmet-wearer's status, ensuring comprehensive safety management.

6. WE CLAIM

Claim 1: A proactive safety system for two-wheeler vehicles integrated with warning indicators for addressing unsafe and over-speed driving incidents comprising:
A helmet-integrated sensor module configured to detect helmet usage, drunk driving, head movements indicating drowsiness, and non-alertness;

A vehicle-integrated sensor module positioned at key locations to gather data on single-hand driving, acceleration, braking, and vibrations;

Raspberry Pi-based advanced algorithmic analysis system processing data from said sensors to identify instances of unsafe driving behaviours and over-speeding;

A GPS-enabled speed limit calculation module determining the vehicle's position and calculating appropriate speed limits for different road conditions;

And a visual warning indicator activation system triggering visual warning indicators (Blue colored LED for unsafe driving and GREEN colored LED for Over-speed) on the vehicle to alert nearby vehicles of potential danger based on the detected unsafe driving behaviours and over-speeding.

Claim 2: The safety system of claim 1, wherein said visual warning indicators activation system includes high-intensity BLUE and GREEN colored LED lights installed on the front and rare sides of the two-wheeler vehicle, synchronized with the severity of detected unsafe driving behaviors and over-speeding to emit timely and appropriate warnings to nearby vehicles and pedestrians.

Claim 3: The safety system of claim 1, wherein said visual warning indicators are activated in response to unsafe driving behaviors such as single-hand driving, sudden acceleration, harsh braking, and sharp turns detected by the Raspberry Pi-based advanced algorithmic analysis system.

Claim 4: The safety system of claim 1, wherein said visual warning indicators are activated when the vehicle exceeds the predefined speed limit or designated speed limit for a particular area as determined by the GPS-enabled speed limit calculation module, thereby alerting nearby vehicles of potential over-speeding hazards.

Claim 5: The safety system of claim 1, wherein single-hand driving condition can be detected by this invention and generates an Unsafe driving signal.

Claim 6: The safety system of claim 1, wherein two separate Raspberry Pi units are used to handle the helmet-integrated sensors and vehicle-integrated sensors. These units communicate through Bluetooth to generate the unsafe and over-speed driving signals.

7. ABSTRACT

The present invention relates to an advanced safety system integrated with visual warning indicators for two-wheeler vehicles, designed to enhance the rider safety and toalert nearby vehicles by proactively addressing unsafe driving behaviours and overspeeding. The system includes a Helmet Integrated Sensor Module (HISM) capable of detecting helmet usage, alcohol consumption, drowsiness, and non-alertness through real-time monitoring of the rider's condition. A Vehicle Integrated Sensor Module (VISM) is positioned at critical points to capture data on single-hand driving, sudden acceleration, harsh braking, and mechanical vibrations. These sensors are connected to two separate Raspberry Pi units - one for the helmet sensors and one for the vehicle sensors - which communicate via Bluetooth and having Internet capability. The data is analyzed using advanced algorithms to detect unsafe driving instances, including single-hand driving, and generate alerts. Additionally, a GPS-enabled module determines the vehicle's location and calculates appropriate speed limits for different road conditions. Based on this data, the system activates visual warning indicators, with a GREEN LED alerts overspeeding and a BLUE LED indicating unsafe driving. This dual Raspberry Pi configuration ensures wireless, real-time analysis and warning signals, offering a strong, proactive safety solution that aims to reduce accidents and enhance road safety for all vehicles and passengers.
, Claims:WE CLAIM

Claim 1: A proactive safety system for two-wheeler vehicles integrated with warning indicators for addressing unsafe and over-speed driving incidents comprising:
A helmet-integrated sensor module configured to detect helmet usage, drunk driving, head movements indicating drowsiness, and non-alertness;

A vehicle-integrated sensor module positioned at key locations to gather data on single-hand driving, acceleration, braking, and vibrations;

Raspberry Pi-based advanced algorithmic analysis system processing data from said sensors to identify instances of unsafe driving behaviours and over-speeding;

A GPS-enabled speed limit calculation module determining the vehicle's position and calculating appropriate speed limits for different road conditions;

And a visual warning indicator activation system triggering visual warning indicators (Blue colored LED for unsafe driving and GREEN colored LED for Over-speed) on the vehicle to alert nearby vehicles of potential danger based on the detected unsafe driving behaviours and over-speeding.

Claim 2: The safety system of claim 1, wherein said visual warning indicators activation system includes high-intensity BLUE and GREEN colored LED lights installed on the front and rare sides of the two-wheeler vehicle, synchronized with the severity of detected unsafe driving behaviors and over-speeding to emit timely and appropriate warnings to nearby vehicles and pedestrians.

Claim 3: The safety system of claim 1, wherein said visual warning indicators are activated in response to unsafe driving behaviors such as single-hand driving, sudden acceleration, harsh braking, and sharp turns detected by the Raspberry Pi-based advanced algorithmic analysis system.

Claim 4: The safety system of claim 1, wherein said visual warning indicators are activated when the vehicle exceeds the predefined speed limit or designated speed limit for a particular area as determined by the GPS-enabled speed limit calculation module, thereby alerting nearby vehicles of potential over-speeding hazards.

Claim 5: The safety system of claim 1, wherein single-hand driving condition can be detected by this invention and generates an Unsafe driving signal.

Claim 6: The safety system of claim 1, wherein two separate Raspberry Pi units are used to handle the helmet-integrated sensors and vehicle-integrated sensors. These units communicate through Bluetooth to generate the unsafe and over-speed driving signals.

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