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.
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.
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.
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.
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 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.
The entire proposed concept will have four steps or modules:
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.
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.
The APIs transfer the data to preprocessing module, which has three sub-modules to prepare the data for analysis.
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.
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.
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.
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:
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.
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:
The parameters for the driver’s health are set at three levels-
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
S. No. | Driver’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Heartbeat | Danger | Need to hospitalize |
2 | Eye Tracking | Danger | About to faint |
3 | Breath Rate | Danger | Need to hospitalize |
Final Decision | Autonomous Mode Activated; all controls are immediately taken from the driver and controlled by the car computer. The car searches for the nearest hospital, calls the emergency services and reaches the hospital. |
Medium Danger Level
S. No. | Driver’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Heartbeat | Normal | Need to alert |
2 | Eye Tracking | Danger | About to faint/Is sleepy or drunk |
3 | Breath Rate | Normal | Need to alert |
Final Decision | The car will immediately alert the driver about fatigue and advice them to take rest for some time. If the driver doesn’t pay attention, the car will stop for some time to allow the driver to rest. |
Normal Level
S. No. | Driver’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Heartbeat | Normal | No alert |
2 | Eye Tracking | Normal | No alert |
3 | Breath Rate | Normal | No alert |
Final Decision | The driver’s condition is good. Enjoy the drive. |
The parameters for the vehicle performance are also set to three risk levels:
Engine’s condition, tire pressure, fuel, braking system, and suspension are the parameters for vehicle performance.
High Danger Level
S. No. | Vehicle’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Engine Performance | Danger | Need maintenance |
2 | Tire Pressure | Low | The tire needs to be inflated |
3 | Breaking | Normal | |
4 | Suspension | Normal | |
5 | Fuel | Normal | |
Final Decision | The car will alert the driver about the engine’s performance and tire pressure. The car will search the nearest service stations. Based on the priorities of the vehicle’s performance, it will check whether the car can make it to its garage or would need a maintenance towing van. |
Medium Danger Level
S. No. | Vehicle’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Engine Performance | Medium | Only alert |
2 | Tire Pressure | Normal | |
3 | Breaking | Normal | |
4 | Suspension | Normal | |
5 | Fuel | Low | Only alert |
Final Decision | The car will only alert the driver about the performance. |
Normal Level
S. No. | Vehicle’s Parameters | Predicted Risk Score/Level | Final Decision by Car |
1 | Engine Performance | Normal | |
2 | Tire Pressure | Normal | |
3 | Breaking | Normal | |
4 | Suspension | Normal | |
5 | Fuel | Normal | |
Final Decision | The car is in good condition. |
There are numerous advantages of using the proposed model to provide emergency support to drivers:
There are a few disadvantages to the proposed model. However, there are solutions to overcome the hurdles.
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.