AI in Self-Driving Cars: Shifting Gears to Autonomy

AI in Self-Driving Cars
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Making self-driving cars possible is among the many things artificial intelligence (AI) has accomplished and continues to do. In self-driving vehicles, AI helps with navigation and self-optimization but also introduces new safety requirements.

To illustrate, in April this year, Dubai hosted the first-ever Abu Dhabi Autonomous Racing League  (A2RL) race, in which eight AI-powered cars equipped with advanced sensors and cameras competed. Although the event faced technical challenges like spins and crashes, it showcased the potential of this technology and highlighted the current state of autonomous vehicles

In this feature, we will talk about AI in self-driving cars, its benefits, and current use cases.

Video source: YouTube/Interesting Engineering

What Is a Self-Driving Car?

An autonomous vehicle, also known as a self-driving or driverless car, is designed to take over some or all driving tasks from a human driver. It navigates from point A to point B, avoids obstacles, and adapts to traffic conditions independently.

Now, let’s look at the six levels of driving automation outlined by the Society of Automotive Engineers (SEA). At Levels 0-2, humans fully operate the car, but it’s loaded with extra gadgets like blind spot warnings or automatic emergency braking.

Under certain conditions – i.e., on certain roads and with a human ready to take over if needed – the car can do all the driving aided by an automated driving system (ADS) at Levels 3 and 4. However, it still needs human intervention in complex situations. And then there’s Level 5: full autonomy. At this final level, no input is required from anyone inside; every car is entirely self-sufficient. 

Although Level 4 is currently under testing, self-driving cars can only become fully commercialized autonomous vehicles by attaining Level 5.

How AI in Self-Driving Cars Works

Major contributors to the creation of self-driving cars have been computer vision (CV) and deep learning. High-resolution cameras and LiDAR (light detection and ranging) are used in CV to monitor the car’s environment by detecting obstacles that may lead to crashes. 

Nevertheless, this alone does not suffice; the vehicle must also understand and follow traffic rules, which is done with the help of machine learning (ML) driven by deep learning. Complex neural networks process information from a car’s sensors, enabling it to conduct more detailed image analyses. 

Manufacturers’ nuanced understanding helps them create advanced self-directing systems. These systems allow intelligent self-driving cars to safely navigate even highly populated urban areas with significant perception levels.

Another step forward in this direction has been made possible through generative AI. By generating new data from existing datasets, this technology enables self-driving vehicles to anticipate and respond better to different driving scenarios, thereby refining their decision-making capabilities. This makes them more trustworthy and safe for public use.

Furthermore, countless driving conditions can be simulated thanks to generative AI, which lets vehicles learn and adapt without real-world risks involved. Through extensive testing environments like these, safety concerns are addressed, speeding up autonomous vehicle development and deployment.

However, there are also some ethical concerns regarding the integration of generative AI into self-driving cars which is fairness and neutrality of decision-making.

The Benefits of AI-Powered Self-Driving Cars

A self-driving car is a great feat of engineering both technically and operationally; convenience, enhanced mobility options, efficiency gains, and cost savings are some of its advantages. They have the potential to make getting around on a day-to-day basis quicker and simpler.

Let’s look at other benefits that AI-powered self-driving cars bring:

Vehicles can operate 24/7

For businesses, self-driving cars can save time and money by allowing drivers to concentrate on more complicated tasks. These vehicles can work without stopping throughout the day, thereby decreasing the need for humans and cutting down operational costs. Autonomous cars can be in motion all the time, which means they provide nonstop service that helps increase productivity.

Fewer Accidents and Safer Roads

Safety is a significant benefit here. AI systems never get tired, intoxicated, or distracted, so they are much less likely – if at all – to cause accidents. Vehicles with artificial intelligence can always keep an eye on what is happening around them and react faster than any human driver, thus leading to lower numbers of crashes and making roads safer.

Mobility for All

Moreover, these cars offer essential mobility for people who cannot drive due to old age, disability, or lack of access to public transport. Cabs without drivers and other autonomous transit vehicles could help such individuals get to workplaces quickly, run errands, or attend medical appointments easily.

Traffic Congestion 

Some cars and trucks already use ADAS to anticipate and avoid dangers. As the National Highway Traffic Safety Administration (NHTSA) reports, these systems help reduce traffic congestion and mitigate accidents, thus improving road safety. 

Environmental Impact

Additionally, self-driving cars promise significant environmental benefits. They can reduce traffic jams and lower the number of vehicles on the road, leading to decreased energy use and fewer greenhouse gas emissions.

Video source: YouTube/Motor Minds

Current State of Self-Driving Cars

At this moment, none of the automakers are selling self-driving cars, even though a few of them claim they are just around the corner. But many brands are making huge strides here – notably by blending high-tech safety systems that assist with acceleration, braking, and steering to reduce driver fatigue.

Currently, the highest level of consumer-available automation is Level 2, found in systems such as GM’s Super Cruise, Ford’s BlueCruise, and Full Self-Driving from Tesla. They allow drivers to let the car handle some driving under certain conditions, but they must stay attentive and ready to take over.

Video source: YouTube/FutureCurious

Several companies are leading the way in the commercial sector with self-driving vehicles for deliveries and robotaxi services. Google-parent Alphabet’s Waymo is No. 1 here after launching its robotaxi service in cities like Metro Phoenix, Los Angeles, San Francisco, and Austin. 

Video source: YouTube/Waymo

Other companies include:

  • Gatik (autonomous trucks for deliveries from distribution centers to stores), 
  • Kodiak Robotics (outfitting semi-trucks with autonomous tech for long-haul routes; working with clients like IKEA and Kroger) and 
  • Swedish company Einride (which uses autonomous electric pods for deliveries in Europe and North America; they’re driven remotely without cabs for human drivers).

The Road to Full Vehicle Autonomy

Achieving full self-driving capability is harder than it seems, and their adoption is slower than expected despite the hype from car manufacturers and enthusiasts. Whether or not cars will ever be able to drive without any human input at all still remains ambiguous.

McKinsey predicts this shift will take time. By 2030, they expect only 4% of new passenger cars to have Level 3 or higher automation. This is projected to grow to just 17% by 2035.

Nevertheless, this should not slow us down because, apart from the advancement of technology itself, regulators need to endorse and gain public trust and acceptance of autonomous vehicles. Before these cars can be widely embraced, different areas must be investigated, such as safety, reliability, infrastructure adaptation, and the formulation of new laws. 

Looking forward 20 years from now, we realize that today’s manufactured automobiles will still dominate our roads. These vehicles are partly automated but not fully autonomous, making it a gradual process to switch from them to self-driving ones; thus, sharing highways with human-driven vehicles over extended periods remains unavoidable. This means there will be a mix of self-driven machines and people-operated cars being used at any given time, creating new challenges for us.

In other words, when humans drive alongside robots, things can get messier than ever before. Simply put, this situation demands constant technological advancements combined with regulatory improvements supported by infrastructural updates if we are to ensure smoothness and safety on the roads.

AI in Self-Driving Cars: Key Takeaways

AI drives self-driving cars. It helps them find their way around, decide things, and improve safety. These vehicles use high-resolution cameras and LiDAR systems, which are supported by computer vision and deep learning to detect obstacles and follow traffic rules. Generative AI also helps by creating different driving scenarios that enable the car to learn and adapt itself with no real-world risks involved.

The advantages of self-driving cars are many. They can operate throughout the day, every day, cut down on accidents, increase safety levels, offer mobility to non-drivers, and reduce traffic jams, among other things, while having a positive environmental impact. 

Although fully autonomous vehicles at Level 5 are still being worked on, AI advancements made so far are paving the path for these machines to be part and parcel of our transport system.

With the growth of AI technology comes the need for ethical considerations so that it may be used justly without any form of bias. 

Still, such challenges should not deter us from exploring what AI can do for self-driving cars because, if developed further, they will create safer roads and more environmentally friendly means of transportation than what we have now.

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Neil Sahota
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.