AI and Personal Vehicles

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 uses data from satellites to figure out where the vehicle is on Earth. Mapping algorithms use 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. Then they combine this data with real-time information from users. This includes things like how fast they drive along the route. These two types of data can give people accurate and precise information about their trip. The AI can even help drivers get around traffic and avoid construction.

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. It does this using a variety of sensors, including cameras and infrared sensors. 

Some systems also help with the task of driving. This can include specific functions, like control systems that adjust the vehicle's speed or steering. It can also include more general functions, like using machine learning models to provide decisions based on different traffic conditions. All of these functions send data to a central data hub. Then that information is used as more training data for future models.

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 a large remote database. The data is then used to build different light-timing scenarios, which are analyzed to determine the best settings. City planners can also use machine learning to design better road systems. This could include things like building roundabouts instead of traffic lights.

AI and Road Safety

Did you know that more than 1 700 people die from car accidents each year in Canada? When looking at the whole world, this number jumps to approximately 1.3 million people. And that is not counting the more than 20 million people who suffer non-fatal injuries each year.

Three common causes of car accidents 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 and bad. 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.

Safety is the most important reason people are developing 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 could 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.

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.

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. Which is why an autonomous car needs a powerful computer. This computer must also 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 coyote? If they do, the coyote 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 coyote, 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 similar decisions. 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.

A related issue to this is bias in the training data used to develop ML models. ML models are only as good as the data that goes into them. One example of this is the data used to train the systems self-driving cars use to detect and avoid pedestrians. The datasets used to train these systems need to be extremely large and diverse to cover every possible size, shape, and skin tone of human beings. If they aren't diverse enough, we could end up with systems that are better at identifying pedestrians with some skin tones than others. This could mean certain groups of people are in danger, based on their skin tone or other factors.

In summary

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!

Let’s Talk Science appreciates the contributions of Melissa Valdez Technology Consultant from AI & Quantum for revisions to this backgrounder.