ARTIFICIAL INTELLIGENCE What is machine learning in autonomous vehicles?
Autonomous or self-driving cars are beginning to occupy the same roads the general public drives on. That can make many people nervous about a vehicle’s ability to make safe decisions. Understanding one of the core technologies used in autonomous vehicles – machine learning – can help settle the minds of the wary.
In order for autonomous vehicles (AVs) to safely navigate streets, whether empty or in rush-hour traffic, requires the ability to make decisions. For AVs, algorithms take the place of a human brain in determining the correct action to perform. Whether a left turn or right, applying the brakes at a stoplight or accelerating after a turn, algorithms need to make these decisions within a fraction of a second.
It’s different than typical programming in that machine learning algorithms are environmental. Data is collected from its immediate surroundings and correlated with previous trips and a set of rules to determine how best to proceed.
Why is machine learning important in AV?
The intention is that self-driving cars will make roads safer because they can make better, more reliable decisions than a human mind. A car must ‘learn’ and adapt to the unpredictable behavior of other cars nearby. Machine learning algorithms make AVs capable of judgments in real time.
This increases safety and trust in autonomous cars, which is the original goal. Without machine learning algorithms, an AV would always make the same decision based on its circumstances, even if variables that could change the outcome were different.
Four subsets of machine learning
The different types of machine learning can be broken down into one of three categories:
- Supervised learning is monitored data that is actively looking for trends and correlations. It’s the type that predicts products you might be interested in on Amazon based on your previous clicks.
- Unsupervised learning is the algorithm searching for patterns without a defined purpose. It sifts through mounds of information to find patterns.
- Reinforcement learning uses a human-like trial-and-error process to achieve an objective. It analyzes possible outcomes and makes a decision based on the best one, then learns from it.
As you can see, machine learning begins to take on reasoning processes much like people do, which is why it works for AVs.
Functions machine learning performs in autonomous driving
Currently, machine learning is in an intermediate stage were it has begun to become mainstream thinking but has not yet become commonplace. Autonomous development has shown that machine learning can be successfully and reliably used for virtually all mobility functions when it’s been implemented. Here are a few of the real-world uses you can see today.
A human drive can’t predict which routes are going to be congested based on a combination of real-time data and compiled data from the past. With machine learning algorithms, an AV’s navigation system can assign the fastest or shortest route based on the conditions surrounding the vehicle as well as any previous information, experienced or shared from other users. It can realistically trim minutes off a commute time.
As an algorithm perpetually making decisions based on immediate surroundings and past experiences, machine learning can perform safety maneuvers faster than a driver can react. And while a human driver might be able to perform one evasive maneuver, AVs could potentially perform complex actions where a human could not avoid a collision. This can help keep pedestrians safer plus avoid distracted driving accidents more often.
A user’s in-cabin experience can be enhanced with machine learning. When you skip a song, it can change satellite radio stations for you when the disliked song is about to be played. It can also tune into your favorite podcast automatically or suggest a nearby fuel station when it detects your fuel level is low.
Undoubtedly, parallel parking and tight perpendicular parking are a source of frustration for many drivers. Powered by machine learning algorithms, an AV can detect its surroundings and park itself without driver input. It can also leave a parking space and return to the driver’s position driverless, allowing parking spots with tighter tolerances to be used.
The implications for machine learning are vast and multifaceted. As autonomous driving progresses, you’ll start to see technology getting ‘smarter’ because of it.