Jack Dreyer | Monday 17th April 2023 4:00pm
With the meteoric rise of Teslas and other electric vehicles, we’ve also seen the similarly meteoric rise of advanced driver assistance systems – even to the point of, possibly, viable self-driving cars. But how do they work, and are they safe?
Let’s find out.
Self-driving relies on ADAS
Advanced Driver Assistance Systems (ADAS) are the super-futuristic siblings of parking sensors. What started as simple beeps when you reversed too near a tree has turned into a whole host of fully-integrated sensors, cameras, and computers.
The idea behind ADAS was initially to make life much easier for drivers: if you have trouble parallel parking, then the ADAS system can do it for you, parking much faster and more safely than many drivers could achieve. Though this has, of course, come with its fair share of scrapes and dents.
The other big advancement – which was more in-demand in the States due to the far greater level of motorway driving there – has been the actual driving elements like automatic braking and lane assist. These features let you put the car in essential auto-pilot while on a motorway until you need to take action – which results in a much less stressful drive over long distances.
How has ADAS turned into self-driving capability?
We should be careful to note here that self-driving cars do not yet “work” in the strictest sense of the word. We’ll get more onto this in a moment, but the essential attempt to replace the need for an active driver with autonomous driving capability has had to rely on ADAS advancements alongside other, more sci-fi ones, like AI and processing power.
And this last element is crucial. When a human driver drives, they’re constantly processing astronomical amounts of information in order to spot hazards and make decisions – and this often needs to happen in split seconds to avoid crashes. Until quite recently, processors simply weren’t fast enough to make decisions so quickly. Now that they have become fast enough to do so, the issue has been in perception and decision making.
This is because of a fundamental difference between how AI systems have been taught to make decisions and how humans learn to make decisions. If we are aware of an object in the way, let’s say a lorry trailer, we’ll also be able to make that decision if it’s a lorry we’ve never seen before, a new model, or one that’s just landed from space.
AI systems, however, still have a long way to go when it comes to this lateral thinking.
So how does self-driving work?
The simplification of the process is that a central computer takes inputs from various cameras and sensors around the car. It then uses those inputs to make decisions about how to drive.
The software that powers this is usually one that will have been trained on huge image datasets of what other vehicles & vehicle types look like, what common road markings look like, what pedestrians crossing roads look like, and so on.
Often, the computers generate 3D understandings of the world around them that will include a certain distance around them. These can be helpful for understanding obstacles (even if it doesn’t categorise those obstacles correctly) because it’s often useful to be able to be aware that something solid is in front of you.
Self-driving cars aren’t yet ‘autonomous’
So this seems very advanced, but the hugely important thing here is that self-driving cars aren’t yet autonomous. The software may have a dependable knowledge of, say, traffic cones and be able to ignore road markings in favour of the traffic cone directions, but what if you’re in a different region that uses different traffic cones?
Tesla, for example, is quite vocal after a string of crashes that their cars aren’t autonomous and require driver supervision at all times. Because the software is still in its infancy. While the cars technically can drive on their own and without supervision, this poses such a threat to the safety of other road users that it’s still treated with great caution almost everywhere in the world.
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