Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn.
Very basically, a machine learning algorithm is given a “teaching set” of data (historical data), then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, “this is a boy” and some of which say, “this is not a boy.” Then you could show the computer a series of new photos and it would begin to identify which photos were of boys.
Machine learning then continues to add to its historical data or we can say teaching set. Every photo (data) that it identifies — correctly or incorrectly — gets added to the teaching set, and the program effectively gets “smarter” and better at completing its task over time.
Autonomous vehicles and self driving are fully integrated into such a system to make the machine work automatically while understanding the nearby surroundings and real-world scenario.
AI-based face recognition and biometric system helping to keep track the human beings and provide a safe zone to live. Security cameras and other surveillance equipment are widely used to keep the cities and habitat safe.
Few years back Google and Tesla have successfully tested autonomous vehicles, even Tesla motors provide a different level of autonomy but were not successful enough, due to few accidents that happened while on testing and at the time of real-life use by the car owners.
Do you know why autonomous vehicles are still not on the road, or what are the reasons it is taking this much time to make such vehicles successfully run on the road?
There are different levels of autonomy in the self-driving cars allowing the driver to control the key functions or depend on the machine to make its own decision.
So, right here before we discuss the challenges of autonomous vehicles we need to know about the different levels of autonomy — a self-driving car use to run on the road.
5 Levels of Autonomous Driving
Level 0: This level, you can say nothing to do with automation, means, all the systems like steering, brake, throttle, and power are controlled by humans.
Level 1: Yes, the level of automation starts from this stage. At this stage of autonomy, most of the functions are still controlled by the driver, but a specific function (like steering or accelerating) can be done automatically by the car.
Level 2: In this stage of automation, at least one driver-assistance system is automated like acceleration and steering, but requires humans for safe operation. Actually, at this level, the driver is disengaged from physically operating the vehicle.
Level 3: At the third level of automation, many functions are automated. Yes at this stage the car can manage all safety-critical functions under certain conditions, but the driver is expected to take over when alerted due to uncertain conditions.
Level 4: This is the stage you can say a car is fully autonomous that can perform all the safety-critical functions in certain areas and under the defined weather conditions. But not all the functions.
Level 5: If a self-driving car is equipped with the 5th level of automation, it is a fully autonomous vehicle, capable of self-driving in every driving scenario just like humans control all the functions.
These are the most common five levels of automation, a self-driving car can be developed. If you want to enjoy a ride on a fully autonomous car, it should have the 4th or 5th level of automation.
But there are many challenges in developing and running a fully autonomous car, and below we will discuss these challenges and their implications.
Tesla and Artificial Intelligence
“We develop and deploy autonomy at scale. We believe that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware is the only way to achieve a general solution to full self-driving.” — stated by Tesla on its official site
Unlike its competitors, Tesla’s biggest USP isn’t just producing automobiles but also technologies. Tesla specializes in making use of high tech technologies to deliver luxurious long-range electric automobiles.
“The advantage that Tesla will have is that we’ll have millions of cars in the field with full autonomy capability and no one else will have that,”
— Elon Musk (chief executive officer of the electric-car maker Tesla)
Throughout its journey, AI and Big Data have remained steady partners of the firm. Tesla has taken excellent use of AI and Big Data for expanding its customer base. The firm has made use of existing customer databases for its data analytics using it to comprehend customer requirements and regularly updating their systems accordingly.
In the case of AI, Tesla has leveraged it to focus on mainly 2 areas: All electric propulsion and autonomous driving.
Recent AI Tools leveraged by Tesla
Initially, Tesla had collaborated with Nvidia to optimize it’s AI integrated chips. Later dropping Nvidia, the company vowed to create its own chips. With these chips, the firm aims to ensure that the cars are able to navigate through not only the freeways but also through local streets as well as traffic signals.
In a recent Hot Chips conference Tesla confirmed that its performance has largely boosted owing to the heavy optimizations in the AI chip. A massive number of transistors have been used — 6 billion — which constitute the processing circuitry for each of Tesla’s chips.
Tesla’s in-house expertise in case of software and battery manufacturing has also helped in giving it an edge over its fellow manufacturers. The firm’s new AI technology aims at setting a milestone for mass-market automation for cars.
Acknowledged as one of the most aggressive developers in the market, Tesla has always been a firm that has made data collection and analysis the biggest wielded weapon for everything it does. When it came to developing their own chips, they made no exception. Artificial intelligence and data analysis has enabled the company to design autonomous cars with the potential to revolutionize the way we drive cars.
Elon Musk has claimed 2020 to be the year for Tesla to release its full self-driving system built on Autopilot.
5 Major Problems with Self Driving Cars
Few automotive manufacturers like Tesla are already integrated certain level of automation into the cars, but not level 5 or full automation, as there are certain challenges of autonomous vehicles making difficult for the manufacturers to develop an AI-enabled fully automated car that can run without human intervention with complete safety.
Understanding the issues with self-driving cars is very important for machine learning engineers to develop such an AI-enabled vehicle for successful driving. So, right here we also discuss the most critical problems with self-driving cars.
- Training AI Model with Machine Learning
As we know, to develop an autonomous vehicle, a machine learning-based technology used for integrating AI into the model. The data gathered through sensors can be understood by cars only through machine learning algorithms.
These algorithms will help identify objects like a pedestrian, a street light detected by the sensors and classify them, as per the system’s training. And then, the car uses this information to help decide whether the car needs to take the right action to move, stop, accelerate, or turn aside to avoid a collision from objects detected by the sensors.
And with the more precise machine learning training process, in near future machines will be able to do this detection and classification more efficiently than a human driver can.
But right now there is no widely accepted and agreed basis for ensuring the machine learning algorithms used in the cars.
There are no such agreements across the automotive industry how far machine learning is reliable in developing such automated cars.
2. Open Road with Unlimited Objects
Autonomous cars run on the road, and once it starts driving, machine learning helps it learn while driving. And while moving on the road, it can detect various objects that have not come across while training and be subject to software updates.
As the road is open, and there could be unlimited or multiple types of new objects visible to cars, that have been not used to train the self-driving car model. And how to ensure that system continues to be just as safe its previous version.
Hence, we need to be able to show that any new learning is safe and that the system doesn’t forget previously safe behaviors or something like this, the industry yet to reach agreement on.
3. Lack of Regulations and Standards
Another hurdle for the self-driving car is there are no specific regulations or sufficient standards for the whole autonomous system.
Actually, as per the current standards for the safety, for existing vehicles, the human driver has to take over the control in an emergency.
For autonomous vehicles, there are few regulations for functions like automated lane-keeping system.
And there are also international standards for autonomous vehicles that include self-driving cars, which sets related requirements but not useful in solving the various other problems like machine learning, operational learning, and sensors.
4. Social Acceptability Among the People
Over the past year while testing or in real-life use, self-driving cars involved in the crash on autopilot mode. And such incidents discourage people to fully rely on autonomous cars due to safety reasons.
Hence, social acceptability is not acceptable to such car owners but also among other people who are sharing the road while running on the road with them.
So, people need to accept and adopt the self-driving vehicle’s systems with involvement in the introduction of such new-age technology.
And unless the acceptability reached social levels, more people will not use to buy self-driving cars, making it difficult for the auto manufacturers to further improve the functions and performance of such cars.
5. Use & Availability of Data for Sensors
To sense the surroundings of an environment, a self-driving car use abroad set of sensors like Camera, Radar, and LIDAR. These sensors help to detect varied objects like pedestrians, other vehicles, and road signs.
The camera helps to view the object while on the other hand, Radar helps to detect objects and track their speed and direction.
Similarly, there is another important sensor called LIDAR that uses lasers to measure the distance between objects and the vehicle.
And a fully autonomous car needs such a set of sensors that accurately detect objects, distance, speed, and so on under all conditions and environments, without a human needing to intervene.
Some other use-cases of AI
- Data Security
Malware is a huge — and growing — problem. Institutional intelligence company Deep Instict says that each piece of new malware tends to have almost the same code as previous versions — only between 2 to 10% of the files change from iteration to iteration. Their learning model has no problem with the 2–10% variations, and can predict which files are malware with great accuracy. In other situations, machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.
2. Personal Security
If you’ve flown on an airplane or attended a big public event lately, you almost certainly had to wait in long security screening lines. But machine learning is proving that it can be an asset to help eliminate false alarms and spot things human screeners might miss in security screenings at airports, stadiums, concerts, and other venues. That can speed up the process significantly and ensure safer events.
3. Financial Trading
Many people are eager to be able to predict what the stock markets will do on any given day — for obvious reasons. But machine learning algorithms are getting closer all the time. Many prestigious trading firms use proprietary systems to predict and execute trades at high speeds and high volume. Many of these rely on probabilities, but even a trade with a relatively low probability, at a high enough volume or speed, can turn huge profits for the firms. And humans can’t possibly compete with machines when it comes to consuming vast quantities of data or the speed with which they can execute a trade.
Artificial Intelligence (AI) is an innovative technology which is helpful to fight the COVID-19 pandemic. Machine learning algorithms can process more information and spot more patterns than their human counterparts. One study used when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed. Additionally, machine learning can be used to understand risk factors for disease in large populations. The company Medicition developed an algorithm that was able to identify eight variables to predict avoidable hospitalizations in diabetes patients.
5. Marketing Personalization
The more you can understand about your customers, the better you can serve them, and the more you will sell. That’s the foundation behind marketing personalization. Perhaps you’ve had the experience in which you visit an online store and look at a product but don’t buy it — and then see digital ads across the web for that exact product for days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.
6. Fraud Detection
Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.
You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.
8. Online Search
Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time you execute a search on Google, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success. If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.
9. Natural Language Processing (NLP)
NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.
10. Smart Cars
IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.