IT technology is becoming more important than engines in the automotive industry. Self-driving cars are being driven by tech companies like Google and Nvidia, using GPS and LIDAR systems and artificial intelligence to cope with different road conditions. There are also legal and ethical issues that need to be addressed.
Tech companies driving the automotive industry?
The automobile has become an integral part of our lives and is arguably the most familiar piece of technology that allows us to push our physical limits. In the past, the engine was the most important element in the automotive market, but in recent years, the electronicization of most of the car’s devices and the emergence of state-of-the-art safety systems and user convenience equipment have changed the way consumers and developers look at cars. Now, the most important thing in a car is not the engine, but the “head.” Especially with the emergence of IT, communication, and ultra-large data processing and implementation technologies, self-driving cars, or driverless car technology, are gaining attention. Let’s take a look at driverless cars, which are being actively researched by several world-class companies, including Google and Nvidia.
Driverless cars on the road, how will we deal with them?
Anyone who has ever taken a driver’s license test will remember the tension of the driving test: you have to obey various traffic laws, pay attention to what’s in front of you and around you in order to keep up with traffic and deal with unexpected situations, and make judgment calls to get to your destination. It’s a very stressful process. The task of a driverless car is divided into two main parts: orientation and understanding road conditions.
Google is building GPS technology and LIDAR systems for driverless cars. First, GPS determines the car’s direction of travel by comparing its current location to its destination. The relationship between road connectivity (road closures, new roads, etc.) and the exact location of roads based on maps is essential. Google’s Google Maps, with its global scale maps, road conditions, and street views, is very influential in this regard.
But even once you’ve solved the GPS and navigation, it’s important to have an accurate picture of the various physical conditions on the road. To do this, Google uses sensor equipment called LiDAR. This equipment consists of remote laser systems, sound wave equipment, 3D cameras, and radar equipment to detect distances and hazards between objects on the road. The laser equipment in particular collects information through lasers that bounce off objects from all 360 degrees and reads information 1.6 million times a second.
Also noteworthy is the work of graphics processing unit (GPU) technology specialist Nvidia. Nvidia’s driving decisions are primarily based on information from the car’s 12 cameras, which are analyzed by a tiny image processor. This image processor analyzes the information by breaking down the objects around it into small chunks to determine the meaning of signs, recognize ambulances, and more. It evolves by learning new information through a network, especially when the processor encounters information it cannot analyze. This falls under the field of artificial intelligence called “machine learning,” which is the same learning pattern of the artificial intelligence “AlphaGo. What’s interesting here is that this AI technology, which is the basis for driverless car technology, is being developed by Google, the same company that created AlphaGo.
Who is responsible for driverless car accidents?
Legislation has already been passed in the US states of Nevada, Florida, Michigan, and California to allow driverless cars on the road, but there is still the restriction that there must be a human in the car. If truly driverless cars are ever allowed on the road, there are a number of ethical and legal issues that need to be addressed. First of all, if the car is fully autonomous, the system could be hacked remotely over the network, which could pose a huge risk. Also, in the event of an accident, the owner or creator of the car would need to be held liable. Another problem is that there are an unfathomable number of unexpected situations on the road. For example, there could be sudden road changes or natural disasters.
But it can be solved!
As mentioned above, driverless car technology still has a lot of problems to solve, but with an average of seven traffic accidents per day in Korea alone due to drowsy driving in the spring, it’s clear that driverless car technology isn’t just here for convenience. Compared to the speed of development of AI AlphaGo, the technical limits of autonomous driving systems are predicted to surpass human levels in the near future. Once the legal and ethical aspects have been fully debated, it won’t be long before we see our own cars “driving for us” on the road.
