AI has made some headway in every industry – including the automotive industry. Artificial intelligence uses data and algorithms to replicate human decision-making ability. Algorithms that help the system learn and solve problems independently are deployed across various industries under the automotive umbrella.
Areas in the Automotive Industry Where AI Is Used
Manufacturing
In the production line, robots are deployed to work with humans. These AI-enabled robots learn manufacturing skills like design and part manufacturing. The system is not completely autonomous although it is possible to have the entire plant operated by AI-powered robots in the future.
After Sale Services
AI also helps with some aftermarket services. AI can predict problems related to the engine, battery or another part that may occur in the future.
Some AI-powered insurers also offer some quick services like settling claim settlements to customers through AI.
Transportation
Automotive AI stretches its muscles best in transportation – with advancements like self-driving cars. AI is completely revolutionizing the transport sector, playing a vital role in technologies like driver assistance that are now being widely used in modern vehicles.
Let us dive deeper into the applications of AI in the automotive industry.
Applications of AI in the Automotive Industry
Autonomous Cars
Self-driving cars basically drive themselves with little to no human input. Achieving autonomy is no mean feat because the car essentially needs to reason and act like a human driver, arguably better even.
The idea of self-driving cars has been around since 1939 but it’s only with developments like AI SDK that computer vision techniques like object detection are possible to create intelligent systems that decode and make sense of what they see.
AI SDK basically handles the scaling of data and AI applications. Decoding visual data is what essentially allows a vehicle to drive itself. Just like you see road signs, lane markings, and traffic lights while driving, a self-driving car needs to detect road infrastructure like that and respond to each accordingly.
How do they do it?
The algorithms responsible for this are basically fed a bunch of relevant data while being trained to detect specific objects and then take appropriate action like slow down or turn.
To collect this data, autonomous vehicles use an array of cameras and sensors. For the model to be reliable, it needs to be consistently fed large sets of data.
It is not perfect though with challenges like bad weather making object detection harder.
It is also possible for a self-driving car to come across an unidentified object while out on the road – an unidentified object is one which is not in any of the data sets used to train the model so there is no way for the car to identify the object.
Traffic Management
Living in a city more often than not means having to sit through hours of traffic and struggling to make it to school or work on time. Traffic jams mean wasted time and as they say, time is money. The flow of traffic can greatly impact a country’s economy.
Traffic in large cities is a never-ending exhausting problem. So, how can automotive AI help? An AI based traffic management system can help curb daily traffic problems and reduce driver fatigue.
AI can help reduce bottlenecks, pinpoint and eradicate choke-points that are clogging up roads. Advancements like computer vision and drones have made this possible. The algorithms can track and count freeway traffic with accuracy as well as analyze traffic density. This helps cities to understand what is going on so it is possible to design better traffic management systems.
AI can also be used in managing road infrastructure like traffic lights for instance. It stops on red and as simple as it sounds, some drivers still run red lights and end up causing accidents.
As perfect as the traffic lights system may be, humans are anything but perfect and mistakes do happen sometimes. Autonomous vehicles can solve this problem.
An AI based system can be trained to recognize traffic lights via computer vision models. These models are trained for a wide range of scenarios like poor light and visibility conditions so they are ready for just about any situation. As soon as a car’s camera spots a light and it’s red, the car puts on the brakes.
The system is not foolproof however. Some issues arise when the camera is fooled by other lights like street lights. I don’t have to explain how devastating the results could be.
Pedestrian Detection
Imagine a system that is capable of spotting and detecting pedestrians through video. Imagine a system that could not only detect pedestrians but also understand their intent – for example – are they going to cross the road now? This will go a long way in avoiding dangerous situations.
Passenger detection has always been a problem for AI automotive because pedestrians can be unpredictable so much so that they pose one of the greatest risks to just how successful self-driving cars can be.
The system does not even need to go into the nitty gritty like beards and noses. All that needs to be done is distinguish a human from another object and perhaps understand what they are likely to do next.
