Artificial Intelligence (AI) may seem new. But AI apps have been used in transportation for a while. Many modern vehicles use a Global Positioning System (GPS). This system uses data from satellites to figure out where we are on Earth. GPS uses AI to determine the best way to get from point A to point B.
To do this, AI systems have learned to predict the best routes from huge amounts of data. They then combine this data with data from real-time information from users. This includes things like how fast they drive along the route. The two types of data can then give people accurate and precise information about their trip. The AI can even help drivers get around traffic and avoid construction. They needed to use a lot of data to train these systems to do what they can do today. In the first few years, many people relying on their GPS got lost. Some even drove into lakes!
Did you know?
Machine Learning (ML) is a type of AI. It is used to develop most transportation systems that use AI.
Many of the safety features in modern vehicles use AI. One example is driver assistance. Driver assistance systems help alert drivers to possible dangers. This could be something like beeping when a car is drifting out of its lane. Some systems also help with the task of driving. This includes anti-lock braking (ABS). ABS is a safety system that prevents the wheels of a vehicle from locking up when you push hard on the brakes.
AI and Traffic Monitoring
No one likes being stuck in traffic! So city planners are always looking for ways to improve the flow of vehicles on roads. Installing sensors on traffic lights can help. The sensors send data to an AI system. It can then control traffic lights to keep traffic from piling up at intersections. City planners can also use ML to design better road systems. This could include things like using roundabouts instead of traffic lights.
AI and Road Safety
Did you know that more than 1 900 people die from car accidents each year in Canada? When looking at the whole world, this number jumps to approximately 1.35 million people. And that is not counting the more than 20 million people who suffer non-fatal injuries each year.
There are three main causes of car accidents. These are speeding, impaired driving and distracted driving. To improve road safety, we can use AI systems to identify people who are doing these things. An AI system can look for patterns in people's driving, both good driving and bad driving. We can then teach the systems to look for certain things that are dangerous, like speeding.
Did you know?
Robocar, the fastest autonomous car reached a speed of 282.42 km/h!
AI and Self-Driving Vehicles
Unlike humans, machines do not do reckless and dangerous things. This led people to wonder if self-driving, or autonomous, vehicles could make our roads safer.
Did you know?
Autonomous means to do something alone, without help.
Safety is the most important factor driving the development of self-driving cars. But it is not the only factor. Time is another factor. Imagine if people could use the time spent driving for other more fun or more productive tasks.
Did you know?
Driverless vehicles might seem like a solution to reduce traffic. But a study has shown that people who use driverless vehicles actually spend more time on the road! This is not good news for the environment.
When it comes to cars, there are different levels of autonomy. Most modern vehicles have some features of Stage 2 automation. Some new cars even have Stage 3 or 4.
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There are 6 levels of vehicle automation. Level 0 is no automation, and the driver does all of the driving. Level 1 is driver assistance where the driver gets help built-in safety features. Level 2 is partial automation where the driver does most of the driving but the vehicle takes control in some situations. Level 3 is conditional automation where the vehicle performs some driving tasks. The driver must be ready to take control. Level 4 is high automation where the vehicle performs most driving tasks. The driver might still need to take control. Level 5 is full automation where the vehicle performs all driving tasks, and a driver is no longer needed.
These cars can drive themselves under certain conditions, such as on the highway. It is important to remember that this technology is still new, and not perfect. People still need to keep their eyes on the road while driving in autonomous vehicles.
How do self-driving vehicles work?
To drive themselves, cars need both hardware and software. The hardware is a set of sensors and mechanical parts. It allows the car to sense its environment and provide data for the car’s computer. It is like the driver’s eyes, hands and legs. Software is computer programming. It allows the car’s computer to make decisions. It is like the driver's brain.
Autonomous cars use many kinds of technologies to sense their environment. This includes high-definition cameras, ultrasonic sensors, radar and LIDAR. Radar uses radio waves to detect objects. LIDAR is like radar except that it uses pulses of light to detect objects. These let the car detect traffic lights, people riding bikes, or even a squirrel crossing the street! They are especially useful when weather conditions reduce visibility.
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Autonomous car sensing includes video cameras, lidar, ultrasonic radar, 360 degree cameras, GPS as well as mid-range and long-range radar.
The vehicle’s software also uses information from GPS. This includes where the car is and information like speed limits. This is a lot of information for the car's computer. Which is why an autonomous car needs a powerful computer. It also needs a computer that can process all this information very quickly. Delays in deciding how to move the car could be very dangerous!
Programming Driverless Vehicles
We once thought that by now, everyone would be using driverless cars. So, why is this not happening? It is pretty simple. Creating machines that can make decisions for themselves in a world of humans is tricky.
Sometimes when driving, a driver finds themselves in a difficult situation. For example, a driver suddenly sees a coyote standing in the middle of the road.
On the side of the road is a deep ditch. The driver hopes the animal will run off, but it is not moving. The driver will not be able to stop in time. Should the driver swerve to avoid hitting the coyote? If they do, they might end up harming themselves and their car by going into the ditch. Or should they hit the dog? If they do, the dog may die, but the person and their car would be okay. What would you do?
If you think that making a decision like that is hard, imagine trying to create a computer program to do it! This is exactly what AI engineers are working on. Going back to the example of the dog, do you think that everyone made the same decision as you? How you made your decision depends on your values. In other words, what you think is important.
Not everyone thinks the same things are important. A study of data gathered through MIT’s moral machine proved this.
The moral machine is a set of car accident scenarios in which people decide what they would do given a choice. You can try it yourself using the link above. The study found that people around the world made some decisions in common. People preferred to save people rather than animals. They preferred to save more lives over fewer lives. And they preferred to save children instead of adults. They also noticed some differences between countries. These likely have to do with what things people in a certain country value. For example, some countries value their elders more than others.
The role of AI in personal vehicles is increasing. Someday these vehicles will probably be the norm. The next generation of people may not even learn how to drive!