Autonomous Vehicles: The Future of Driving
By: Karthik Mittal
Tags: Automation, Autonomous Cars, Computer Vision

How Google’s self-driving cars look. They’re literally everywhere!

You’ve probably seen these Google vehicles everywhere; it seems that whenever you go on the highway, one of these vehicles is always right next to you. It’s amazing how fast technological advancements in cars have progressed in the past years, and the crazy thing is that these advancements show no signs of stopping.

There’s a lot of confusion to how far self-driving cars have progressed and what they are planning to do next due to the limited marketing done by these different companies. After researching this topic more, I found out that we are much further in automating vehicles than I believed we were.

How Can We Classify Automation?

Brief descriptions for each of the SAE levels of automation (from no automation to full automation).

To understand present vehicles and how far they have progressed, we need a standard in order to gauge the autonomy of cars in a driverless environment. Typically, the SAE levels of automation provide a good measure for this, consisting of six levels.

The Preliminary Levels

The Ford Model T car (the most popular car in the early 1900s)

The zeroth level (no automation) forces each action done by the car to be operated by the human driver; these types of cars were seen in the late 19th century when cars were first proposed to the general public.

For most of the 1900s, these cars were shown to be the pinnacle of innovation, but technological advancements by curious inventors like George Rashid (making the automatic braking system) led to further automation in cars and a promotion to the first level. These cars can only handle one task at a time, which makes them inefficient but still better than the earlier cars.

Many cars today have the second level of automation (partial automation) where they can do steering, braking, and accelerating but still need the driver with hands on the wheel.

The Higher Levels

A Waymo self-driving car (mainly located in Phoenix right now).

The leap between the second and third level is tremendous, but it has been accomplished by companies like Tesla which have Level 3 features. Particularly, the autopilot feature, which can mainly be used on highways, can enable the car to act autonomously, but the car needs constant attention and immediate intervention by the driver if something goes wrong.

Now, the thing that surprised me and prompted me to write this article is learning that there’s a company that has achieved Level 4 automation. Waymo, a company spawned off of Alphabet, has been testing driverless cars with no safety drivers in the seat of the car for more than a year and has driven over ten million miles. It’s mind boggling that we have already reached this level of automation; however, these cars only work in places in which they are compatible. These specific geographical areas need to be extensively mapped beforehand in order to optimize safety, so Waymo’s cars are only in a few cities around America.

Still, this finding has led me to hope that we may achieve Level 5 automation in the future, where cars can act autonomously on any environment with absolutely no driver presence. To understand how these self-driving cars actually can perform these operations as well as drivers, a brief overview of its process is necessary.

How Do Autonomous Vehicles Work?

The five steps that automated vehicles take to ensure safety and efficiency in the road.

Autonomous vehicles use a variety of sensors and different technologies in order to understand their environment, make predictions, and coordinate a path. There are five main processes that these vehicles follow constantly in order to ensure optimal safety and efficiency: computer vision, sensor fusion, localization, path planning, and control.

Understanding the Environment

The different sensors/cameras and their subsequent ranges to monitor the environment around the car.

Computer vision allows the computer in the autonomous vehicle to understand the environment mostly using cameras. In order to classify objects surrounding the car, it performs image classification with convolutional neural networks. To understand this classification, check out my Medium article where I go in depth about CNNs and their fundamental components.

Computer vision gives a cursory overview of the environment; however, these vehicles also incorporate data from other sensors to further clarify their understanding of the surrounding environment and map out the speed of objects coming towards them in a process called sensor fusion. Some common sensors used in these vehicles are radar (emitting radio waves to detect objects within several meters using the Doppler effect) and LiDAR (emitting light sent out from a laser to generate a cloud of the environment). Both of these sensors are extremely interesting and have many applications, so comment below if you want me to write a Medium article on these!

With all this new information, computers are able to focus on determining their position relative to the other elements on the map, using a technique called localization. Traditional GPS is not feasible for performing this as it only maps to approximately 1–2 meters, which could lead to unsafe conditions. Providing the vehicle with a map could be another potential solution; however, problems like repaving of roads and the addition of plant life next to roads necessitates the constant updating of these maps. Therefore, vehicles often have to rely on these sensors with centimeter level accuracy to map their location relative to their environment as it is the most efficient solution.

Taking Action From Data

After finding its position, it then uses the data from the first two steps to predict what obstacles will face them to generate trajectories from points A to B in an efficient and safe manner. This step is the one most researched as these scientists try to understand how to take the incoming data to chart a path; currently, machine learning optimization algorithms seem like the path to go.

Lastly, after charting the path that the car needs to take, the car uses controllers to change the vehicle’s mechanics to fit the trajectory plotted. This step, often called control, uses a concept called control theory which tries to figure out how to apply force to an object to control its movement. Controllers run an algorithm to decide how to change the steering, throttle, and brake in the car in the correct manner.

What’s Next for Autonomous Vehicles?

There are a vast amount of applications to the field of automation that can solve existing problems in the world. Unmanned aerial vehicles can be used to monitor climate change, gather information about natural disasters, and go on search and rescue missions on challenging terrain. Autonomous boats can be used in tandem with collision avoidance systems and artificial intelligence to ensure safer rides and build more optimal paths so that boats take less energy. Autonomous tankers can be used to mitigate human error as tankers are often the leading vehicle for car accidents.

In the field of self-driving cars, Waymo is starting to expand their platform to other cities so that autonomous vehicles start to become more mainstream in the world. Companies like GM plan to build a mass production assembly plant to make the process of building fully autonomous vehicles autonomous. Ride-sharing services like Uber are planning to integrate self-driving cars with their model to ensure for more efficient pickups.

This field is only just starting to be explored, and I am interested where this will be taken in the next ten years. Comment below what your thoughts are!

TL;DR

Self-driving cars have six levels of automation from the zeroth level (no automation) to the fifth level (full automation).

Many cars today have Level 2 automation; however, companies like Waymo are currently building Level 4 vehicles in select geographical areas.

Self-driving cars follow a procedure consisting of five processes constantly: computer vision, sensor fusion, localization, path planning, and control.

Automation can be applied to other vehicles as well like boats, tankers, and aerial vehicles. It is only just starting to be explored and will only continue to grow in the later future.

Additional Resources

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