How Connected Cars and Quantum Neural Network Can Help Drivers in Emergencies

Quantum neural networks are built based on quantum computing and classical physics to deliver accurate and reliable predictions. Our proposed model will make cars self-capable to handle emergencies. We’ll discuss the internal working and modules of our quantum neural network model for connected cars. 

Quantum neural networks are created using the principles of quantum mechanics. These are typically feed-forward networks where the information collected in the previous layers is analyzed and forwarded to the next layer. Deep neural networks (DNNs) are already used in developing autonomous vehicles to define the right driving behavior for a vehicle. A Quantum neural network aims to assist drivers in effectively handling emergency situations. 

Providing emergency support to car drivers using connected cars and quantum neural networks can reduce the risk of accidents and help drivers reach their destinations faster. This can potentially save lives, especially when driving to hospitals or emergency units. Quantum neural networks are more reliable and accurate than conventional neural networks. 

Introduction to Connected Cars and Quantum Neural Network Model 

A new system/ device will be embedded in the vehicle’s dashboard to provide support to drivers in emergency situations. The system offers second-to-second continuous support and is specifically designed to handle emergency or complex situations. The system collects data from the vehicle’s sensors and sends it to the connected cloud drive. This data will be processed using quantum neural networks built on the principles of quantum computing and classical physics.

The concept of using quantum neural networks and connecting cars (through shared cloud data) is different from the existing approaches used in the industry. This model will be faster, reliable, accurate, and efficient enough to handle the worst-case scenarios people might experience when driving. 

Resources Required for the Model 

Data is the primary resource for this model. The quantum neural network model requires data from three sources to understand the situations, driving behavior, and the vehicle’s overall performance. 

Descriptive Data 

This data is about the car and its performance. The data is collected from sensors embedded in the engine, suspension, brake, tires (air pressure), etc. This data will be used to identify car’s health and quality. It also provides information about what’s happening in the car every second. The quantum neural network model will be able to provide a suitable solution when it knows the car’s strengths and limitations. 

Navigational Data 

This data is related to the routes, navigations, and trips you take in the car. The model collects data from maps to determine the current location, destination, route map, etc. It also gathers data from side impact detection sensors, blind spot detection sensors, cyclist and pedestrian detection sensors, etc., to pinpoint your exact current location. 

Behavioral Data 

Behavioral data deals with drivers’ performance and abilities. The data is extracted from the sensors embedded in the dashboard. Different sensors are used to collect data necessary for the quantum neural network model to understand the driver’s health and current condition. The sensors help determine who the driver is and suggest a solution according to their driving history (collected and stored in the connected cloud). 

Heartbeat sensors, eye-tracking sensors, and fingerprint sensors on the steering wheel are used for data collection. Sensors that track the driving patterns are also used to determine the abilities of the driver.

Workflow of the Proposed Model

Working Process of the Quantum Neural Network Model 

The entire proposed concept will have four steps or modules: 

• Extracting Data
• Preprocessing Data 
• Training and Predicting Outcomes 
• Presenting the Data 

 Each module has a definite purpose and streamlines the data flow within the model to arrive at the desired outcome. The second module is where the majority of the work happens. It is divided into three sub-modules. Let’s explore each module in detail. 

1. Data Extraction

As the name suggests, the data collected from multiple sensors in the car and stored in the cloud are extracted into the APIs. The process of collecting data from the car’s sensors and sending them to the connected cloud drive is continuous. The vast amounts of data are then directly sent to the APIs, where preprocessing occurs. 

2. Data Preprocessing 

The APIs transfer the data to preprocessing module, which has three sub-modules to prepare the data for analysis. 

Data Cleaning 

The first sub-module cleans the data extracted from the connected drive APIs. This is a necessary step to improve data quality and increase the accuracy of the quantum neural network model. 

Naturally, data collected from multiple sensors will have issues such as wrong image frames, incompatible data formats, corrupt data values, incomplete/ missing data values, etc. This will affect the quality of the final outcome.

This sub-module uses different techniques and tools to clean data and repair the wrong image frames. It tries to resolve the missing/ incomplete data or remove it totally. Statistical techniques are used to identify the issues with data and clean it accordingly. 

Data Preprocessing 

Preprocessing is similar to structuring and formatting data in large datasets. This sub-module prepares the cleaned data to make it ready for transformation, training, and predictions. The data is categorized based on its source. 

For example, data from the cameras are sent to the video processing module. Data from heartbeat sensors go to the numerical processing module, and so on. New data categories will be created to sort the cleaned input data into neat segments/ types, making it easy for the quantum neural network to process. 

Data Transformation 

The last sub-module of the preprocessing stage is data transformation. Here, the preprocessed and sorted data is transformed to create a summary of what it contains. This helps understand the actual meaning of the data before it is fed into the quantum neural network for predictions. The transformed data is analyzed to arrive at the summary and is fed into the learning phase of the system. 

3. Training and Predicting Outcomes using Quantum Neural Network Model 

This module deals with training the quantum neural network to become capable of working with large datasets and delivering accurate predictions in less time. The data transformed in the previous module is fed into the quantum neural network. This network is different from the conventional neural network as it is built on the principles of quantum computing and classical physics. 

Qubits are used to speed up the process in the network. It is also reliable and more accurate. The intent of this module is to train the quantum neural network system to become self-capable when handling large datasets. We check and test the predicted outcomes after the training is complete. Testing the predicted outcomes will help determine the efficiency of the model. We can make the required changes and adjustments to improve the network’s accuracy. 

The predicted outcomes contain the predicted risk scores of each sensor in the car. The outcomes give a summary of all the predicted scores in two categories: 

• The risk score of the vehicle’s performance 
• The risk score of the driver’s health 

These two factors play an imperative role in emergencies. Once the results are analyzed, we test the system with data from other cars to determine if the tested system is accurate and reliable. 

4. Data Presentation 

 In this final module, we focus on presenting the predictions to the driver in an understandable format. We feed the final predicted data into the data presentation phase and display it on the system embedded in the car dashboard. The predicted data is fed in real-time for the driver and passenger to know the current situation and the possibility of what could happen soon based on the risk scores of the car’s performance and the driver’s health. 

We can further streamline the process and make the ride safer by setting up parameters for the driver’s health and car performance. The car will automatically take control and make decisions if the driver’s health (or car’s condition) has deteriorated to trigger an action from the system. The car will behave like an autonomous vehicle to reduce the risk of accidents and save the lives of the driver and the passengers. 

For example, the car will take autonomous decisions and drive itself to the nearest hospital or emergency unit if the driver’s heartbeat/ breathing rate/ eye-tracking movement fall below or above the normal zones. 

The car can make different decisions based on the type and extent of the danger. It is primarily classified into the following: 

Driver’s Health 

The parameters for the driver’s health are set at three levels- 

• High/ danger level where the driver is in no condition to make decisions or perform independent actions- the car’s system will activate the autonomous mode and do the needful to prevent accidents and save lives. 
• Medium risk level where the system sends a warning to the driver about the possible risk and suggests an action- the car’s system will wait for the driver to take the action, failing which, it will activate the autonomous mode and do the needful. 
• Normal risk level where everything seems good- the car’s system will not send any alerts or disturb the driver. 

Here, the parameters we choose will depend on the sensors fitted in the car. The general driver-health parameters are heartbeat, eye-tracking, and breathing rate. 

High Danger Level 

Medium Danger Level 

Normal Level 

Vehicle Performance 

The parameters for the vehicle performance are also set to three risk levels: 

• High/ danger level where the engine is faulty, the tire pressure is low, or there’s an issue with the brakes, suspension, etc., that can lead to potential accidents- the car’s system will search for the nearest service center or mechanic. It will also determine if the car can reach the new destination or if it will have to be towed. 
• Medium risk level where there’s a cause of alarm in the engine/ tire performance and needs inspection- the system will alert the driver about the risk but will not take an action until the risk level touches high. 
• Normal risk level where the car’s components are in good condition and don’t need any maintenance or servicing- the system will remain silent and send no alerts. It will continue to collect data and monitor. 

Engine’s condition, tire pressure, fuel, braking system, and suspension are the parameters for vehicle performance. 

High Danger Level 

Medium Danger Level 

Normal Level 

The Architecture of the Proposed Quantum Neural Network Model 

Advantages of Developing the Proposed Quantum Neural Network Model

There are numerous advantages of using the proposed model to provide emergency support to drivers: 

• Quantum neural networks are faster than conventional neural networks.
• It can handle structured and unstructured data. 
• It can read data in different formats and categories (numerical/ video/ etc.)
• The predictions will be more accurate. 
• The system is designed to handle worst-case scenarios. 
• It can activate the autonomous mode and make decisions when necessary. 
• It is easy to implement in the existing infrastructure of the connected drive system of cars from brands like BMW. 

Disadvantages of Developing the Proposed Quantum Neural Network Model

There are a few disadvantages to the proposed model. However, there are solutions to overcome the hurdles. 

• The current parameters are not enough to measure the driver’s health. Additional health sensors should be fitted to the car. 
• The dashboard interface should be redesigned to cover all aspects of the detailed report. 

Final Words 

The proposed model is a great choice for providing emergency support to car drivers using connected cars and quantum neural networks. It is faster, better, and more accurate. It provides data and alerts in real-time to keep the drivers and passengers up to date with the situation. 

Drivers who suddenly fall ill behind the steering wheel or fail to notice a major problem with the vehicle will have higher chances of averting the danger and coming out alive. It can reduce accidents and improve safety for the people in the vehicle. 

Rohit Narain

Rohit is a Data Scientist at DataToBiz, having over 5+ years of experience in the field of Data Science, Machine Learning and NLP. He has published over 5 research papers in reputed International Journals and National conferences. When not working, you can find him engrossed in designing automobiles and learning about new innovative technologies.