For a while now, there has been a lot of noise around AI technology, most especially in cars.
Most people who drive cars every time know that a lot of focus, calculation, and absolute control of the stirring wheel together with the other driving components is important when driving a car.
And most of the time, the main cause of accidents is mainly attributed to human errors.
To reduce the rate at which accidents are occurring, the integration of AI technology into the car system makes it an achievable goal.
Humans are known to always make errors due to distractions of the mind or focus. And this can’t be fully solved in driving unless allowing the introduction of AI technology in cars.
The AI technology currently used in cars is the self-driving model. This AI technology called; “Self-driving models” is the AI technology that moves people’s cars without them touching the stirring.
It has an analytical ability that it uses to make decisions when moving the vehicle on the road. The AI technology in cars is put under a series of training.
The goal of the training is to make the software know how to move the car properly on the road. And also, for its analytical ability in driving to improve.
So, the number of mistakes it can make will fall drastically to almost zero. Also, this training is done to make AI in car technology become a lot better.
Truth be told, using AI car technology is not a bad thing or a good thing. The reason is, both scenarios have their side effect.
Looking at the situation of things in the country. Most especially the issue of the hike in prices of fuel in the country caused due to the war between Ukraine and Russia.
It has made a lot of people find it difficult to move their cars every day. The price of fuel has skyrocketed and most people can’t afford the daily expenses.
In a city whereby, there are lots of cars using fuel gases, it has become a lot more difficult to purchase gas for one’s car.
For this reason, AI technology in cars is even experiencing an even greater speed in its development.
As companies like Tesla who have made cars using AI technology and many others, are consistently pumping funds into the production and upgrading of AI technology in cars.
For the experience of AI technology car users to be great, the pros and cons of this kind of technology should be known.
This way you can enjoy your smart car to the fullest. That’s why in the next few lines, you get to know some information about AI car technology and then the pros and cons of AI in cars.
Role of AI in the Automobile Industry
The integration of AI technology in the automobile industry is a smart move made by some top automobile companies.
The likes of Tesla have used AI technology to make unbelievable cars. They have made cars that can do unimaginable things that only happen in the imaginative world.
AI has been used in making traffic system intelligence. This way, the right traffic system for a road is easily determined and the rate of traffic jams is reduced as well.
AI technology has made driving much more interesting. Car owners can now operate their vehicle work with ease, all thanks to the introduction of AI technology.
Using machine learning algorithms, smart cars can now make certain decisions on their own. It can know how to adapt to rain conditions.
Especially in cases whereby it is not rainy before and suddenly it starts to rain.
Artificial intelligence also helps cars to be able to follow traffic rules and regulations in real time.
With this feature, the road becomes free of accidents. As driving becomes much safer, the efficiency and convenience that cars offer normally increase.
In the aspect of electric and hybrid car development, AI technology makes the creation process easier and smoother.
It gives mechanical engineers insights into how to make cool cars’ internal components. This way every internal component of the car part is maximized to the fullest.
Therefore, causing an increment in the efficiency and performance level of the car.
With the utilization of AI technology in the automobile industry, there is much more improvement that is still going to happen.
The future of automobiles is optimistic about the impact AI technology is going to bring for it.
Ways AI technology is used in the Automobile industry
1. In Sensing and Perception
Using AI technology in car Industries has made certain improvements to occur. AI can sense its environment if proper training is mete to it.
With this, smart sensors such as smart cameras, AI LiDAR for detecting the structure of the environment using lighting impulses, radar, and ultrasonic sensors for perceiving sound, etc.
A lot of data are gathered automatically. These data are then processed to be used for each respective scenario.
With different training done on the different smart sensors, the sensors become more knowledgeable and better at how it uses the analyzed information. So, therefore, creating a smooth driving experience.
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2. In making smart driving decisions
Before the introduction of AI technology, car owners drive their cars themselves. And during the driving process, some impromptu decisions occur.
This is because these decisions were not planned at all. With this sort of experience, AI technology is now trained to make certain decisions when used in automobiles.
It can gather data using the sensors. And then process the information using it AI algorithm. The results of the processed data are then incorporated into driving sessions when necessary.
Some of these decisions include slowing down when the traffic system gives a red light.
3. In making predictive models
AI technology has helped Automobile industries to make smart cars that can make predictions. And the predictions are not baseless, they are always backed by surrounding situations.
It can predict the movement of other people’s cars. Thereby making it to know how t avoid hitting them from behind or from the front.
It is also able to predict if a pedestrian is going to move at a zebra crossing point or not.
This predictive ability allows this AI software in smart cars to be able to prepare their next moves for any possible scenarios that might want to occur.
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4. Integration of the Natural Language Processing Model
The natural language processing model is an AI technology known for speech. It is generally known for communication.
This AI technology is equipped with voice recognition features that allow car owners to give orders to their smart cars.
AI technology has gone far ahead in this NLP model. Car owners can communicate in their normal language and this model can understand the intent of the message passed across to it.
5. Creation of a smart driving experience
Nothing is as comfortable as sitting quietly in your moving car without using the steering wheel. Busy men and women who work their self out due to much workload.
Most times get tired by the end of the day and therefore won’t want to drive their cars. The invention of smart cars solves this major problem. As they don’t have to do anything in the car.
They just have to sit down and do whatever they want that doesn’t require driving. Companies like Tesla has created such kind of car.
And to be realistic with you, these cars are very good on the road and are expert drivers. As they have sensors on their body that can sense everything in the environment inaccurate measure.
In a general sense, AI technology has made life easier for mainly automobile users. The things, AI does in cars as solve a lot of problems that car owners face.
With the proper training given to AI technology for cars, car owners can comfortably drive their cars easily.
Does AI use Deep Learning in Car Operation?
This is a type of machine-learning algorithm that can process large volumes of data. It trains the neural networks on the AI system using a large database containing tons of data.
The goal of the training is to make the neural networks to be able to learn and recognize the patterns in large data.
So it can easily take out its information from the large volume of data. In making smart cars, the AI technology used always has Deep Learning models embedded deep in its interface.
This model allows it to be able to perform certain tasks with ease. Smart cars can now perform tasks like image and speech recognition, natural language processing, and predictive modeling easily.
To create a more reviving experience in the car, AI technology uses it DeepL model in its interface to make smart decisions.
It makes the car move in the right direction and makes driving swirl as when due.
In cases whereby there is a tendency that there will be traffic, the informant AI model and DeepL model work together to get information from the news and then analyze possible route that has no traffic.
It is responsible for the backbone behind smart decisions made by the smart car technological system.
Should Automobile industries use GDDR6 in Cars?
GDDR6 is a graphic processing memory fully known as “Graphics Double Data Rate 6”. It stores any graphics information and also processes it to produce useful information that becomes useful to the smart car system.
When a smart car is moving on the road, its sensors take in graphical information. This information is now used in making decisions while on the move.
It takes an incredible graphics processing model like GDDR6 to be able to process the graphical image and then provide the necessary information within a very short time.
GDDR6 although a good memory processor of graphics does not use much energy to do it work efficiently.
Even though it takes in data from all the sensors in the car system that can transmit graphical images such as LiDar, cameras, radar, etc.
It still doesn’t consume much energy. This way energy is preserved for smart cars to use as they don’t use fuel like the manual and automatic car systems.
GDDR6 is an Automobile technology that can provide powerful processing services for smart car systems.
Looking at the fact that there is no solid replacement technology for it now. It is sure that this AI model is going to get more advanced in its processing speed.
Such enables fast and efficient processing capacity of large volumes of data.
Automobile AI algorithm learning style
There are two main types of automobile AI algorithm learning styles used to train the algorithm and they are mainly Supervised and Unsupervised learning styles.
Although they are two different learning styles for algorithms, they are both utilise by the AI technology in the Automobile system.
A. Supervised Learning Style
In this style of learning, the machine learning algorithms are trained using a large database that contains both the input and output.
Meaning that the input and output have already been set. What remains, is for the AI algorithm system to learn both the input and output data.
This model is trained accordingly to the input and output placed in the database of the AI technology.
When training the AI algorithm in this software, the trainer presents the model with both the input and output data.
This is so that, the AI can learn the pattern in both the output and input.
This training is done continually until the AI technological tool can take in the exact input and then provide the exact output without no changes.
During the training, the AI algorithm is supplemented with optimization algorithms.
This optimization algorithm’s job is to help the AI algorithm to be able to generate new output similar to the output it is being trained with.
Whenever it is given a data input that is somewhat similar to the original data input it is being trained with.
We know you are probably wondering why should they train the AI in a supervised manner.
Well, the thing is, AI operates based on the data on its large database. Just like the human brain, if there is no forehand information, the brain can’t do anything or create new things.
The same principle goes for AI technology as well. It is just that AI is not as powerful as the human brain. AI technology tool gets the pattern of the data both input and output.
And since its algorithm is working with that of the optimization algorithm. The results they both hold are amazing.
What task does Supervised Learning support?
The task that this learning style support are grouped mainly into 3.
This task includes;
1. Classification
A task that requires classification normally always has particular characteristics that are common to them alone.
This characteristic can be anything. It is these special characteristics that make this particular task different from the other task.
Therefore, this task with common characteristics is grouped as a class label.
2. Regression
This is the second task that this learning style suits. Any task that is centered around the generation of continuous value is what this task is all about.
With the training, the algorithm can easily predict the values of a particular input data.
3. Structured Prediction
In this case, the algorithm of the car system is trained to know how to predict a sequence of input data.
The algorithm is trained to take in input data and then it now unravels the pattern in the data. It is after it unravels the pattern that it can predict the full sequence of the input data.
Use case of Supervised learning
Supervised learning is one of the good ways to train AI car models. As AI is not intelligent enough to figure out the specifics of some matters, that is why supervised learning is needed to train it.
With this kind of training, AI car models get better at what they are doing. In the next few lines, you will get to know instances where supervised learning can be used;
1. In cases of object recognition
AI car models need supervised learning for this. As they are just like a newborn baby who is just seeing an object for the first time.
Certainly, the brain at that point is just developing. The AI model is like that as well. If it is not trained to know objects it won’t be able to identify them whenever it sees one.
For example, AI car models don’t know pedestrians, traffic lights, other vehicles, road signs in images, lidar point cloud road signs, etc. Unless the trainee introduced these objects to it system.
2. In cases of modeling
Supervised learning is also good for training AI models to be able to predict some circumstances. This is not something an AI model can do out of the blue without any forehand training.
The need of supervised training is needed to teach the AI model this task. Predicting the likelihood of a particular event is possible if that event always happens when a particular activity takes place.
Introducing the particular activity that usually triggers the other events to the AI model makes it able to predict the likelihood of the event happening.
For example; pedestrians always want to cross the road at the zebra crossing point. So, anytime the AI model in the car sees a zebra crossing it waits for pedestrians to cross to the other side.
3. In cases of predicting behaviors
Even though this task sounds similar to modeling, they are not all that similar. Supervised learning help to train the AI car model to know and understand the behavior of other car drivers.
It also helps them to know the behaviors of pedestrians as well. Once, the car model receives the training it gets better at predicting the behaviors of road users.
B. Unsupervised learning
Unsupervised learning is the training of AI models using data that are not labeled. This means that all the data provided to the AI model are just streams of unorganized data.
The goal of using this kind of training is to train the AI model to know how to analyze a large stream of data.
And for it to properly do this, it has to undo the pattern of each data. It is only when it understands the pattern of each of the data that it can successfully analyze the data.
The training is not focused on making the AI model generate output, it is just focused on identifying patterns alone.
Unsupervised learning is not used for classifying data. They are instead used for finding the pattern in different data formats.
It is after identifying the patterns that it now forms clusters, i.e., grouping data with similar patterns together.
After this clustering, it now removes the unnecessary information present in the data. So, the important ones are left behind.
Also, it is trained to know how to pick up anomalies in the data introduced to its system. Anomalies are the data that are different from the other data.
So, when the AI model notices it, it picks that data as an anomaly.
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Use case of Unsupervised learning
Unsupervised learning also has ways in which it is put to good use. Just like how supervised learning has its good use, the unsupervised learning approach also has its good use as well.
In the next few lines, you will get to know how an unsupervised learning approach is helpful to AI technology cars.
1. In anomaly detection
Unsupervised learning approach is very helpful in this aspect. As there’s little to no impact that the supervised learning approach can make in this aspect.
But with an unsupervised learning approach, it is a different ball game. Introducing streams of unorganized data help to train the car AI model to know and understand the pattern.
Once the AI fully understands the pattern in the data, any data that has a different pattern is an anomaly automatically.
For example; the unsupervised learning approach trains the AI model to know how to identify car drivers turning their car abruptly at a point wrong for making turns.
Another instance is the case where a pedestrian crosses the road at a point where there is no zebra crossing.
2. In making clusters
Unsupervised learning algorithm is good for training the Car AI sensors. It helps in training them to collect and group data of similar patterns together.
It takes in similar instances, be it road surface data, weather conditions, traffic conditions, and many others, and then it forms a group for them.
3. It helps in extracting features
Unsupervised learning approach trains AI models in smart cars to be able to extract important features.
Looking at the fact that AI in smart cars always experiences a massive influx of data using an unsupervised learning approach.
With the frequent introduction of this large stream of unorganized data, it sorts out important features.
And with this frequent training, it gets better at taking the important information in the data stream.
So when the smart car is moving on the road it can take in lots of data, sift out the important ones, and then dispose of the unnecessary ones away.
This way, the storage is filled with important data only, and the storage memory is preserved for other important data.
For example, the AI sensors in smart cars can pick up important data in Lidar cloud points. It can be an object or anything else.
All the sensors do is take in the objects in a scene showing different things.
Pros and Cons of AI in Cars
Pros of AI Technology in Cars
1. It helps in reducing the rate of accidents
The introduction of AI technology in cars has helped in reducing the rate of accidents in cities. Most of the accidents that happen on the highway are attributed to human errors.
Human sometimes gets distracted when driving, or they drive when they are drunk, etc. All these cases are human errors that result in accidents on the higher.
Since it is not everyone that is using smart cars, the rate of accidents has reduced to a certain extent.
2. It helps improve traffic flow
Every one of us has at one point in time been a cause of traffic congestion. It might have been done knowingly or unknowingly.
But regardless, you still cause traffic congestion. But with AI smart cars, it is different. The rules and regulations of road traffic have already been integrated into the car system.
This way, the smart car doesn’t disobey the rules and regulations guiding road usage. And in cases whereby there might be traffic congestion on the road.
The smart car plugs into the traffic news and then makes real-time decisions on beat route to pass to beat the traffic.
3. It increases the rate of movement
Using cars that possess AI technology is very helpful for movement. Most especially in moving aged adults, people with disabilities, etc.
With AI cars, the occupants don’t have to do much to move the car and get to their destination.
4. It provides cool environmental benefits
Cars with AI technology help in keeping the environment air clean. In a location where about 90% of occupants have a car.
It is guaranteed that the air in that area is polluted and there is an increasing tendency of environmental hazards.
But with AI cars, it is different. There is no need to buy fuel from a nearby gas station to move your smart car.
Since, the car moves using technology, it doesn’t emit fumes. Thereby causing the environmental air to be safe for inhaling.
Cons of AI Technology in Cars
1. It isn’t completely reliable
Cars that use AI technology are not completely reliable. Even though they provide comfort to their users, they are still lacking in some things.
There are instances that the AI car won’t move due to some weather conditions that it isn’t trained to work with.
Also, there is the issue of safety. Most drivers are very experienced in driving so therefore don’t trust AI to keep them safe in unpredictable driving situations.
2. It causes loss of Jobs
People who use driving to fend for themselves have higher chances of losing their jobs. The reward is because there is AI technology that does its job.
And since it might be expensive to keep them, companies that need drivers just relieved them of their job. And then employ AI technology to do the task.
2. Issues around ethical and legal circumstances
There are many uncertainties around smart cars. Due to these uncertainties like accidents, malfunctioning, ethics and legal privilege have to be raised for these uncertainties.
The occurrence of these uncertainties is usually bad for both the car owner and the car company. So, these privileges are set to compensate the car owner when these uncertainties occur in the future.
3. Risk of getting hacked
Smart car work using AI technology. And anything AI technology always has a single or group of algorithms functioning together to make it work.
Which means there is a risk of the smart car getting hacked. Causing the control of the car to switch to the hacker. This situation is a dangerous one, especially if there is an occupant inside the car.
As the hacker can control the car to have an accident or damage properties.
Frequently asked questions
Are there any cars using AI technology?
Yea, some cars use AI technology to run their operations. In the next few lines, you will discover some examples of this type of car.
1. Waymo
This is an Automobile company that produces smart cars. It is owned by Alphabet which is the parent company of Google.
The smart cars produced by Waymo are now used on public roads in several cities in the States, not excluding Phoenix, Arizona, and Detroit, Michigan.
2. Tesla Automobile Company
Tesla Automobile Company has a type of Automobile called Tesla Autopilot. This Tesla Autopilot is a semi-autonomous driving system and it is not found in all of Tesla's automobiles.
Tesla did not design this car model to be a self-drive smart car. Yet, it still has a bit of self-driving features like; lane keeping and lane changing. And this task requires minimal input from the driver.
3. Cruise Automobile Company
Cruise is an Automobile Company that also uses AI technology in its automobile designs. It is a car company owned by General Motors.
The self-driving technology design is currently used on San Francisco, Arizona, California, and Phoenix public roads.
4. Aurora Automobile Company
This is an Automobile company using AI technology in developing cars. This company designs autonomous cars that can perform self-driving.
Aurora designs this smart car that uses self-driving technology to suit different categories of users such as passenger vehicles, delivery vehicles, and public transport as well.
These smart cars are in use on public roads in several cities in the United States.
Are driverless cars safe or not?
Both the long drive and the short drive in smart cars are sometimes termed safer than cars driven by humans themselves.
Users just have to input more additional engineering that can prevent the smart car from malfunctioning to make it safe for driving.
Since hackers can hijack it, it is best to play it safe. So, putting these safety measures can help out with preventing hackers from hijacking it.
Do self-driving cars react faster than cars driven by humans?
Cars driven by AI technology tends to react faster than human. And this is very normal as AI doesn't get distracted and so, therefore, has a higher degree of focus.
Research shows that if someone or something suddenly appears on the road, it takes AI cars an average of 0.5 seconds to step on the break.
It is this fast because of its powerful sensors like lidar, radar, and camera system that has powerful zooming ability.
All these sensors are very powerful and are always taking in data from the surroundings to the car system. Unlike a human driver who takes about 1.6 seconds to step on the break.
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Conclusion
There is no likelihood that the price of fuel is going to reduce anytime soon. And as a result, there is going to be more need for smart cars for daily outings.
Truth be told, smart cars have both their advantages and disadvantages just like the other car systems. And as a result, some people are wary of it while others are using it.
And this is understandable. This is why the top producing companies are working diligently on making sure the disadvantages like car system algorithm hacking, malfunctioning, are solved.
So, smart cars can be safe for use. And so far, with the current progress made in AI development.
It has helped with preventing accidents to a greater extent and has also made traveling in cars comfortable.
But regardless, with more improvement it is sure that smart cars are going to offer more benefits to users.
