It may seem odd to discuss the future of self-driving cars by looking back 100 years ago, but hang with us for a moment.
In the early 20th century, the automobile industry began to gain speed. Not only were motor vehicles true disruptors in everyday life, but their production also signaled a seismic change in how the world economy functioned, particularly in manufacturing. In fact, the shift redirected how other industries might evolve, which meant increasing the workforce to meet consumer demand.
Fast-forward to today: The world is on the cusp of another revolution with self-driving cars. Can you see the parallels in how life and the economy might change once driverless transportation is more widely available? Once these products become more prevalent, the economy will demand that more experts enter the field—but this time in technology rather than manufacturing. What kinds of skills, education, and hands-on experience will be necessary to facilitate this massive redirection?
To make self-driving cars a reality, professionals in the coding, programming, and information technology spaces will have to harness the power of AI, machine learning, automation processes, and other smart platforms. With the right training, tech gurus will ensure self-driving cars are not simply a piece of science fiction, but rather another massive leap in how humans move through their daily lives.
Let’s take a look at what to expect from the proliferation of this technology.
What Makes Self-Driving Cars Go?
Although the engine that powers today’s self-driving car is complex, it’s not difficult to understand in theory, especially for those in tech. These engines take advantage of AI’s ability to communicate with the user and associated systems, evaluate unique situations or data sets, adjust processes or actions, and learn from experiences. Coders and programmers must synthesize large amounts of structured data sets from systems like image recognition platforms, machine learning algorithms, and neural networks in order to engineer cars that operate in an autonomous fashion.
For example, SearchEnterprise AI explains that on-board cameras and other sensors produce data that a neural recognition network uses to recognize objects such as trees, stop signs, traffic lights, crosswalks, and other vehicles. The neural recognition network identifies patterns in these data sets and then funnels the information into a machine learning system. The machine learning system then interprets and structures those data sets into information that AI-enabled systems can use to communicate and make decisions based on the data.
What’s truly remarkable about AI is that the technology can execute complex calculations, algorithms, communications, and decisions in fractions of a second, which mirrors a human response time. But what’s even more intriguing from a coding and programming perspective is the potential for maturity within these systems. As self-driving AI successfully navigates real-world situations, its ability to make more nuanced decisions will increase, much like an actual human being behind the wheel.
Tiers of Self-Driving Cars
The very first automobiles did not have all the bells and whistles we’re accustomed to today. It’s safe to say that the Model T did not have air conditioning or power seats! Given the challenges early manufacturers had to overcome, it was at times remarkable that these primitive vehicles actually got motorists from Point A to Point B, and the same is true today (at least for right now) with the state of self-driving cars and the different tiers of automation.
Most of today’s self-driving cars such as Tesla or Google’s Waymo still require some level of human intervention, or at the very least they require a person to be present while in operation. But that begs the question: What are the tiers of self-driving cars, and how do they build upon each other?
According to Toward Data Science, we can break the evolution of self-driving cars into five tiers:
- Tier 0: Consider this your most basic 1950s conception of a car. The driver must initiate and oversee all of the vehicle’s operations. Nothing fancy here.
- Tier 1: Now we introduce minor pieces of automation such as cruise control, assisted breaking, or blind spot detection.
- Tier 2: AI can successfully communicate and execute two simultaneous functions during operation, such as acceleration and steering. At this stage, however, a human driver needs to be present for safety or emergency situations.
- Tier 3: AI can now successfully execute numerous actions and tasks at the same time, such as accelerating, steering, stopping, navigating, and parking. However, human intervention is still required when something unexpected happens.
- Tier 4: The technology is completely autonomous and able to execute all driving-related activities except for extreme or outlying environmental conditions, such as heavy rain or snow.
- Tier 5: The car can successfully operate itself in any situation or context without any human intervention.
The current status of self-driving cars hovers around tiers 2 and 3. So when will AI and its associated network of recognition, communication, and decision-making put us in the proverbial driver seat for tier 5?
In a recent interview, Elon Musk suggested we may be only a year or two away from fully realizing a tier 5 self-driving car. It’s safe to say (at least prior to the COVID-19 pandemic) that the coding and programming capabilities appear to be evolving at a rate consistent with that prediction.
Roadblocks for Self-Driving Cars
Just because technology is moving at a rapid pace, that doesn’t necessarily mean that those who must ultimately sign off on deploying AI are evolving at the same rate. There are a number of roadblocks that AI specialists will need to overcome to make completely autonomous vehicles a reality. These challenges include:
- Legal: In particular, we’re talking about the issue of liability when accidents do occur (because, let’s face it, accidents will still happen with self-driving cars). These are questions legislators and insurance companies will have to tackle in relatively short order to prevent chaos in how such incidents are legislated and adjudicated.
- Cost: Cost is an important consideration both on the manufacturer side and the consumer side. However, let’s focus on the consumer side since that’s likely to be the greatest hurdle. The most reasonably priced Tesla hovers near $40,000 depending on region or market. In fact, the price of the Tesla has been its greatest detractor to converting from fossil-fueled engines to electric cars because the average American family simply can’t afford that expenditure. The same can be said of the first fully automated cars, and that presents a significant barrier for more mainstream car manufacturers like Ford, Chevy, or Toyota, who may balk at producing a car with such a high price tag.
- Technology: We’re saving this one for last because this is where today’s brightest, most talented, and most motivated coders and programmers can actually help usher in a new day for how the entire world drives from Point A to Point B.
As we discussed earlier, the current state of AI with self-driving cars does have a defined ceiling. Severe weather, limited bandwidth for systems to communicate, or bugs within existing systems (hey, Google Maps isn’t 100 percent perfect all the time) can amount to safety concerns, especially in crowded urban areas or on interstates and freeways. Although a variety of sources indicate the percentage of accidents would decrease by as much as 75 percent each year, today’s coders and programmers will be tasked with demonstrating an impeccable track record in safety and security.
In addition, with so much communication and data shared between systems within one car, what’s to stop data and information from being shared between multiple cars (unintentionally, of course), which could then interfere with the operation of other vehicles and result in accidents? Or even result in security breaches in an owner’s identity, financial information, or any other personal data tied to the smart systems within their car?
These are just a few of the obstacles that will give budding programmers an opportunity to shine as they work to overcome them.
Excitement for the Future of AI
So what makes self-driving cars worth overcoming all the challenges standing in their way? Why get excited about the technological shifts heading our way? Job creation and economic growth is one thing. But there are more than enough reasons to get excited about what’s possible when developing AI capabilities. The prospect of fully automated cars brings:
- Increased levels of safety and awareness through the use of external cameras, sensors, and other pieces of detection equipment.
- Predictive and prescriptive maintenance plans based on complex computing and information transmission via AI, which will increase the lifespan of a vehicle.
- Decreases in harmful emissions and other pollutants through the use of AI-driven route creation and other more efficient uses of fuel.
- An enhanced overall consumer and driving experience thanks to the ability of machine learning to adapt to the owner’s preferences and habits.
Despite the many benefits of self-driving cars, the gravity of the challenges often gives consumers pause about actually using this technology. However, with the right education, hands-on experience, determination, and continued growth in AI, programmers are sure to overcome all the obstacles, and it may not be too far into the future before you can grab that quick cat nap on the way to the store to pick up the milk.
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