System for Real-Time Driver Alerts in Low-Visibility Conditions

Abstract

ABSTRACT System for Real-Time Driver Alerts in Low-Visibility Conditions The present invention discloses an driver alert system designed for low-visibility driving conditions. The system integrates LiDAR and ultrasonic sensors to detect long- and short-range obstacles, respectively. A data processing unit utilizes an R-transform method to process sensor data in real-time, enhancing hazard detection. A data fusion technique combines sensor inputs to improve accuracy, while the system generates alerts through LED indicators and a mobile app. Additionally, the system communicates with cloud platforms for machine learning-based improvement of hazard prediction. Powered by solar panels and rechargeable batteries, the system ensures continuous operation in various conditions. The instant invention enhances driver safety by offering adaptive, real-time alerts and is scalable for various vehicle types and environments.

Specification

Description:FIELD OF THE INVENTION
The present invention relates to the field of automotive safety, specifically addressing driver assistance technology that enhances situational awareness in low-visibility conditions, such as fog, heavy rain, and nighttime driving. More precisely, relating to a system for Real-Time Driver Alerts in Low-Visibility Conditions.

BACKGROUND OF THE INVENTION
Conventional driver alert systems include radar, camera-based solutions, and, in some cases, LiDAR for obstacle detection and real-time driver feedback. However, these technologies have significant shortcomings when it comes to reliability under adverse weather and visibility conditions. Camera-based systems, for example, often fail to function effectively in poor lighting or dense fog, while radar systems, though suitable for detecting larger obstacles, lack the precision necessary for mapping nearby environments in high detail. Moreover, existing systems frequently operate independently, without cohesive integration of data from multiple sensors, thereby limiting their effectiveness in accurately assessing complex driving environments.

The present invention addresses these limitations by introducing a highly integrated IoT-enhanced driver alert system that combines multiple types of sensors, such as LiDAR and ultrasonic sensors, which offer reliable performance in both short- and long-range obstacle detection. This system uses data fusion algorithms to combine the information gathered from each sensor, creating a more comprehensive understanding of the vehicle's surroundings. Additionally, the invention incorporates advanced R-transform algorithms to interpret sensor data in real time, even under challenging weather conditions. Through IoT connectivity, the system communicates with cloud platforms and other smart devices, allowing continuous learning, updates, and adaptation based on historical driving data. This results in a robust solution for enhancing driver awareness and improving safety in low-visibility driving situations.

OBJECTS OF THE INVENTION
The primary object of the present invention is to provide a driver alert system capable of functioning reliably in low-visibility conditions.

Another object of the instant invention is to enable real-time, accurate detection of potential hazards regardless of adverse weather or lighting.

Yet another object of the present invention is to enable seamless IoT integration, allowing the system to leverage data analytics, machine learning, and cloud communication for continuous improvements and adaptability.

SUMMARY OF THE INVENTION
To overcome the shortcomings of the prior arts and to meet the objects of the instant invention it is disclosed herein a driver alert system for low-visibility driving conditions, comprising: a sensor module including a LiDAR sensor for long-range obstacle detection and an ultrasonic sensor for short-range obstacle detection; a data processing unit with a microcontroller configured to process data from the sensor module using an R-transform algorithm, translating sensor data into actionable insights; a data fusion algorithm within the data processing unit for integrating data from the LiDAR and ultrasonic sensors to improve hazard detection accuracy; a communication interface including a Node MCU with Wi-Fi capabilities to enable real-time data sharing with external devices and cloud platforms; an alert system comprising LED indicators and a mobile application, configured to provide real-time visual and audio alerts to the driver; and a power management system having solar panels and rechargeable batteries to supply renewable power to the system. The data processing unit integrates machine learning algorithms configured to analyze historical sensor data and improve hazard prediction accuracy over time. The alert system includes a feedback mechanism that allows the driver to accept or dismiss alerts. The communication interface is configured to interact with urban traffic management systems. The power management system is configured to recharge the batteries using solar energy during the day.

BRIEF DESCRIPTION OF THE FIGURES
Figure 1: Shows the flowchart of the system according to one embodiment of the instant invention.
Figure 2: Shows the prototype of the system according to one embodiment of the instant invention.
Figure 3: Shows the block diagram of the system architecture according to one embodiment of the instant invention.

DETAILED DESCRIPTION OF THE INVENTION
The driver alert system consists of multiple interconnected modules, each with specific functions that together enhance driver safety under low-visibility conditions. These modules include:

Sensor Module:
The system utilizes LiDAR sensors, which emit laser pulses to measure distances to objects and map the three-dimensional space surrounding the vehicle. This technology functions effectively even in poor visibility and extreme weather, providing reliable long-range detection.
Ultrasonic sensors are employed for short-range detection using sound waves that bounce off nearby objects, accurately gauging distances. These sensors are particularly useful during low-speed maneuvers, such as parking, to enhance situational awareness for the driver.

Data Processing Unit:
Microcontroller: The microcontroller collects data from the sensors and processes it in real time. By implementing R-transform algorithms, it translates raw sensor data into actionable insights that help predict potential hazards.

Basic Components of the R-Transform:
Input Signal u(n):
R-transform consists of an input discrete time signal u(n) which could be any input signal, for example, sensor values sent from IoT equipment through the driver alert system
Typically, the symbol u(n) represents real-time values where, at every n, a value is tagged as a discrete time point or an index of the data sequence
Summation Operator ∑_(j=1)^∞▒.
The summation over the index j in the R-transform is infinite. This means that at each time step, all past values of input sequences u(j) are considered.
For implementation purposes, this summation may be truncated to a small number of terms to limit real-time computations during its use.
Recursive Decay Factor (1-1/s)^(j+1)
The decay factor: (1-1/s)^(j+1) is an essential part of the R-transform. It weights contribution of past values in a decaying manner, wherein more recent values contribute more to the output at any given time.
This factor makes older data points gradually less significant and recent data more important. This is the very characteristic of the R-transform that makes it especially useful in real-time systems where current data is more important.
Scaling Factor 1/s^2 :
The unit of 1/s^2 is a scaling parameter that scales the strength of the output to be transformed. In addition it, for the same value of transform gives quite flexibility to adjust sensitivity of transform to various applications.
The choice of parameters can be based on the application at hand. This could be to counteract the sensor's resolution or noise of the environment. Scaling has concomitant dynamic behaviour adjustment of the transform
Infinite Sum Interpretation:
Summation from j=1 to ∞ allows the R-transform to consider past inputs, integrating the history of the data sequence u(n). It brings a continuous impact of prior values, an invaluable tool for predictive analytics in time-critical applications like accident prevention.
In practice, the summation can be truncated at some finite number of terms depending on computational efficiency and needs; yet the infinite number underscores the fact that the data is spread out over continuous histories.
Transformation Domain:
The R-transform is to be conceived of as passing data from the time-domain sequence into a transformed frequency-like domain in which the structures, trends, and anomalies within the data become easier to identify.
In applications such as IoT-based driver alert systems, this domain shift helps filter out noise and detect subtle yet critical changes in the sensor data stream.
Time and Frequency Sensitivity:
By rendering the R-transform recursive and decaying, such a transform is prone to both time-domain fluctuations and noise in the frequency domain. By picking up short-term variations or anomalies at real time, it will simultaneously smooth out high-frequency noise over time.

Data Fusion Algorithm: This algorithm integrates data from LiDAR and ultrasonic sensors to construct a cohesive understanding of the environment, increasing the accuracy of hazard detection and minimizing the likelihood of false alarms.

Communication Interface:
Node MCU: A compact microcontroller board with built-in Wi-Fi capabilities, the Node MCU enables the system to communicate with external devices and cloud platforms. This interface allows for real-time sharing of data with the driver's mobile device, the vehicle's infotainment system, or cloud-based analytics platforms. The connection to the cloud also supports advanced data processing, enabling the use of machine learning algorithms to improve the system's predictability over time.

Alert System:
LED Indicators: These provide real-time visual alerts on the dashboard, using flashing lights or color changes to indicate the presence and severity of hazards. This visual cue helps capture the driver's attention promptly, enhancing their response time.
Mobile Application: A companion mobile app provides continuous real-time alerts, allowing the driver to define their preferred alert types and review historical data. It includes a feedback feature, enabling drivers to accept or reject alerts, which the system uses to adapt to individual preferences and reduce false alerts.

Power Management:
Solar Panels: Solar panels supply renewable energy to the system, reducing the reliance on the vehicle's battery. These panels are strategically positioned to receive maximum sunlight exposure when the vehicle is parked outside.
Rechargeable Batteries: Excess energy from the solar panels is stored in batteries, ensuring system functionality during low-light conditions, such as nighttime driving, without depleting the vehicle's power supply.

The present invention features a feedback mechanism that allows drivers to accept or dismiss alerts, helping the system learn individual preferences and reduce false alarms. Additionally, the system is designed to be compatible with various vehicle models and can interact with urban traffic management systems and other smart infrastructure, enhancing traffic flow and safety across networks.


The system operates by continuously gathering data from LiDAR and ultrasonic sensors positioned around the vehicle. This sensor data is processed by the microcontroller, where the R-transform algorithm interprets it to predict potential hazards. The data fusion algorithm integrates the information from all sensors to provide a holistic view of the environment, improving hazard detection accuracy.

Through the communication interface, the system transmits real-time alerts to the driver via LED indicators on the dashboard and a mobile application. The mobile app allows drivers to configure their alert preferences and view historical data, while the system uses IoT connectivity to upload data to the cloud. Here, machine learning algorithms analyze historical patterns to enhance the system's adaptability over time, improving its responsiveness to different driving conditions.

The system is powered by solar panels, which charge the batteries during daylight hours, ensuring sustainable operation even in low-light conditions.

The present invention offers several embodiments, each tailored to different driving environments, vehicle types, and user preferences. These embodiments allow the IoT-enhanced driver alert system to be customized for a variety of applications, ensuring its versatility and adaptability across various scenarios. Below are different embodiments of the invention:

Basic Driver Alert System with Visual Alerts
In this embodiment, the system focuses on providing real-time visual alerts to the driver through LED indicators on the dashboard. This version uses LiDAR and ultrasonic sensors for obstacle detection, with the data processing unit performing basic R-transform analysis to predict hazards. The communication interface in this embodiment is limited to the vehicle's internal systems, providing immediate alerts without cloud-based analytics. This version is ideal for entry-level vehicles or applications where a simple, self-contained driver alert system is sufficient.

Driver Alert System with Mobile App Integration
This embodiment adds mobile application integration, allowing drivers to receive real-time alerts and notifications on their smartphones. The communication interface uses a Node MCU with Wi-Fi capabilities to send sensor data to the mobile app. The app provides customizable settings, allowing drivers to choose which types of alerts they wish to receive. Additionally, the app stores historical driving data, enabling drivers to review past alerts and system performance. This embodiment is suitable for users seeking an enhanced user interface and remote access to alerts.

Advanced System with Cloud-Based Machine Learning
In this embodiment, the system leverages cloud-based machine learning algorithms to improve hazard detection accuracy over time. The data processing unit sends collected sensor data to a cloud platform, where advanced analytics are performed. Historical data is analyzed to identify patterns, enabling the system to refine its hazard prediction algorithms. Through IoT connectivity, this version also allows for remote software updates, ensuring that the system remains current with new safety features and improvements. This embodiment is ideal for applications requiring continuous adaptability and enhancement, such as autonomous or semi-autonomous vehicles.

Driver Alert System for Urban Traffic Management Integration
This embodiment is specifically designed to interface with urban traffic management systems. In addition to providing real-time alerts to drivers, the system communicates with traffic management infrastructure, relaying information about road conditions, traffic flow, and potential hazards. This data can be used by traffic authorities to monitor congestion and improve traffic safety in urban areas. The system's communication interface supports standard communication protocols, ensuring interoperability with various smart city platforms. This embodiment is suitable for vehicles in densely populated cities and public transportation systems.

All-Weather and Off-Road Driver Alert System
This embodiment is optimized for off-road or all-weather conditions, making it suitable for rugged terrains and extreme weather scenarios. In addition to LiDAR and ultrasonic sensors, this version includes a combination of radar and thermal imaging sensors, which provide enhanced visibility in fog, rain, snow, and darkness. The data processing unit is configured with specialized R-transform algorithms that account for adverse environmental conditions, reducing false alarms and ensuring reliable hazard detection. This embodiment is ideal for off-road vehicles, military applications, and vehicles operating in remote or harsh environments.

Solar-Powered, Energy-Efficient System
In this embodiment, the system includes a robust power management unit with high-capacity solar panels and rechargeable batteries. The solar panels charge the batteries during the day, allowing the system to operate independently of the vehicle's power supply. This energy-efficient design is ideal for regions with abundant sunlight, ensuring continuous functionality while minimizing energy consumption. The energy-efficient embodiment is particularly beneficial for long-distance commercial vehicles, reducing power load and operational costs.

Driver Alert System with Adaptive Alert Mechanism
This embodiment includes an adaptive alert mechanism that adjusts based on driving conditions, driver behavior, and vehicle speed. The system analyzes real-time data and modifies the sensitivity of alerts according to traffic density, road conditions, and driver preferences. For example, in high-traffic areas, the system might provide more frequent alerts, while in less congested areas, it may reduce alert frequency to minimize driver distraction. The adaptive alert mechanism learns from driver feedback, enabling personalized alert levels. This embodiment is especially useful in reducing false alarms and improving driver experience.

Vehicle-to-Vehicle (V2V) Communication System
This embodiment supports vehicle-to-vehicle (V2V) communication, allowing vehicles equipped with the system to exchange hazard data and alerts. By sharing real-time information about obstacles, weather conditions, or road hazards with nearby vehicles, the system enhances situational awareness for all drivers in proximity. V2V communication enables proactive responses to hazards and promotes safer driving behaviors across connected vehicles. This embodiment is particularly beneficial for fleets, emergency vehicles, and autonomous vehicles.

Driver Alert System with Predictive Analytics
In this embodiment, the system uses predictive analytics to anticipate potential hazards based on historical data, weather forecasts, and traffic conditions. The data processing unit leverages machine learning models to analyze trends and predict obstacles before they occur, enabling proactive driver alerts. This predictive approach allows drivers to adjust their driving behavior in advance, reducing the likelihood of accidents. The predictive analytics embodiment is ideal for autonomous or semi-autonomous vehicles, as it provides an added layer of safety in unpredictable environments.

Public Transport Safety System
This embodiment is designed for installation in public transport vehicles, such as buses and trams, where passenger safety is a priority. The system includes additional sensors to monitor the vehicle's surroundings and a communication interface that allows it to connect with central monitoring centers. Real-time alerts can be sent to both the driver and the central control unit, enabling coordinated responses to hazards. This embodiment also allows for data collection on route conditions, enhancing safety and efficiency in public transportation networks.

Fleet Management and Logistics System
This embodiment is tailored for logistics and fleet management applications. It includes features for monitoring vehicle routes, tracking obstacles, and optimizing delivery schedules. By collecting data from multiple vehicles in the fleet, the system can provide insights on the safest and most efficient routes, helping reduce travel time and fuel consumption. This embodiment is especially suitable for commercial vehicles that operate in various terrains and require continuous monitoring for safe and efficient operations.

Enhanced System with Biometric Driver Monitoring
This embodiment integrates biometric sensors to monitor the driver's alertness and physical state. For example, eye-tracking and heart rate sensors detect signs of fatigue or drowsiness, triggering alerts if the driver appears distracted or unresponsive. The system adjusts the alert frequency based on the driver's condition, increasing the sensitivity of alerts when fatigue is detected. This embodiment is ideal for long-distance drivers and applications where driver alertness is crucial for safety.

Example Scenario according to one embodiment of the instant invention: Foggy Conditions with Low Visibility
Consider a scenario in which a vehicle is traveling on a highway under heavy fog, causing extremely poor visibility. The driver can only see a few meters ahead, creating a hazardous situation where the likelihood of encountering sudden obstacles, such as slow-moving or braking vehicles, is significantly heightened.

In this scenario, the IoT-enhanced driver alert system becomes essential. The system employs both LiDAR and radar sensors to continuously scan the environment and detect obstacles, even when visibility is critically reduced. As the vehicle moves through the fog, the sensors pick up distance readings that include noise due to environmental interference. Despite the noisiness of the data, the system processes it using the R-transform algorithm, which is designed to emphasize recent, clearer data points while gradually discarding older, noisier measurements.

The R-transform processes the incoming data by applying a mathematical model that weighs recent sensor readings more heavily than older, less relevant data. This ensures that the system focuses on the most accurate information available, reducing the influence of older, noisy data points. The result is a smoothed, real-time interpretation of the obstacle's distance, which enhances the reliability of hazard detection in challenging conditions.

In this scenario, suppose the R-transform algorithm returns an output distance that falls below a predetermined threshold (for example, 5 meters). Upon reaching this threshold, the system immediately raises a warning alert, notifying the driver to reduce speed. The driver receives this alert before they can visually confirm the obstacle due to the low visibility. This early warning allows the driver to take proactive action, such as slowing down or preparing to stop, potentially avoiding a collision.

Overall, each embodiment offers specific features and configurations that cater to different driving conditions, vehicle types, and user requirements. By providing various embodiments, the instant invention ensures wide applicability, scalability, and the ability to meet the unique needs of different users and driving environments. 
, Claims:WE Claim:

1. A driver alert system for low-visibility driving conditions, comprising:

a sensor module including a LiDAR sensor for long-range obstacle detection and an ultrasonic sensor for short-range obstacle detection;
a data processing unit with a microcontroller configured to process data from the sensor module using an R-transform algorithm, translating sensor data into actionable insights;
a data fusion algorithm within the data processing unit for integrating data from the LiDAR and ultrasonic sensors to improve hazard detection accuracy;
a communication interface including a Node MCU with Wi-Fi capabilities to enable real-time data sharing with external devices and cloud platforms;
an alert system comprising LED indicators and a mobile application, configured to provide real-time visual and audio alerts to the driver; and
a power management system having solar panels and rechargeable batteries to supply renewable power to the system.

2. The system as claimed in claim 1, wherein the data processing unit integrates machine learning algorithms configured to analyze historical sensor data and improve hazard prediction accuracy over time.

3. The system as claimed in claim 1, wherein the alert system includes a feedback mechanism that allows the driver to accept or dismiss alerts.

4. The system as claimed in claim 1, wherein the communication interface is configured to interact with urban traffic management systems.

5. The system as claimed in claim 1, wherein the power management system is configured to recharge the batteries using solar energy during the day.

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