Autonomous cars are the future of driving. However, the popular perception of self-driving cars as fully automated vehicles with no need for human intervention, is not entirely accurate. In reality, different levels of automation exist. AI is deployed not just for driver safety but also for passenger comfort & convenience.
For instance, AI accurately reads lips to understand drivers’ intentions amidst noisy environments. Additionally, autopilot is an older vehicle automation feature, and artificial intelligence has now picked up where autopilot left off.
Autonomous vehicle technology has advanced rapidly in the last few years. Ten years ago, it was confined to research labs & academic institutions. During the last five years, Google and a few other companies tested AI. Today, the industry is growing quickly, with approximately 30 to 50 California-based companies already licensed to operate self-driving vehicles.
With autonomous vehicles becoming a more mainstream technology, further innovation is expected, & this industry will continue to develop in the coming years. This has given a massive push to every AI app development company to create new, innovative solutions for autonomous vehicles.
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The United States and China lead the world race for the development of autonomous vehicles. Germany and Japan, despite being countries famous for the cars they produce, are behind. “The fundamental difference is AI, “said Tony Han, one of the founders of JingChi, a Chinese-based autonomous vehicle company.
The two countries also lead the regulations for autonomous cars. There are three megatrends behind all this interest: the growing popularity of electric cars, the emergence of the shared economy that is behind private transport companies such as Uber and Lyft, in addition to the advances of artificial intelligence.
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Most autonomous vehicle companies are developing an appropriate technology for what they call a level 4 roadster. There are five levels of automation for autonomous cars. Level 1 is the most basic of all, it has an autopilot function and has been used for years. Level 5, in which the vehicle is fully autonomous, is the most advanced. Level 4 is a grade below, it is a highly automated level in which the car can operate in certain situations without the intervention or attention of the driver, such as in areas with special barriers or with traffic.
AI inside the car
Nvidia’s senior director for automotive sector, Danny Shapiro, emphasizes the weight of responsibility technology companies carry in the development of autonomous vehicles considering the potential impact on safety, economy & efficiency.
“It is not a recommendation engine for Netflix,” he said during the congress. “AI has to be precise.” This means that it requires “extreme” computing capacity and a lot of code, Shapiro said. In the trunk of autonomous vehicles, there are computers and powerful graphic processing units working with deep learning to analyze all the data that is received, to determine things, for example, if an object located in front of the vehicle is a person, another car, a fire hydrant, etc.
Although some time is needed for autonomous vehicles to reach the market, AI is already transforming the interior of cars. The front cameras can identify people inside the vehicle and track the driver’s eye position to see if they are about to fall asleep or if they are distracted, and can even read their lips. By integrating sensors & cameras, car manufacturers are implementing measures to improve road safety. One example of this is the warning system that alerts drivers of potential hazards like another vehicle about to run a red light at a crossing. Additionally, advanced technology allows drivers to verbally notify one another of impending dangers, like a bicyclist approaching a roundabout. These innovations are a step towards creating a safer driving experience for everyone on the road.
One of the primary goals of autonomous vehicle companies is to enhance safety on the roads by making driving safer.
Human error is responsible for 94% of car collisions, said Jeff Schneider, senior engineering manager at Uber. He noted that 50% of the errors that result in accidents were due to recognition errors, the driver was not paying attention or did not see something approaching. The other 50% occur as a result of a decision error: the driver was driving too fast or misunderstood the situation.
Self-driving cars have the technology to handle errors that occur on the road. Problems with recognizing obstacles are reduced using advanced tools such as sensors, radars, cameras, and Lidar. These tools allow the car to have a 360-degree view of its surroundings & sense the speed of other objects.
With the aid of sophisticated computer systems, the environment is analyzed, & the car makes informed decisions. This technology is essential in ensuring the safety & comfort of passengers when using autonomous vehicles.
One way to contribute to precision is to incorporate redundancy systems. For example, if a traffic signal is hidden or unclear for some reason, a measurement resource is activated to ensure that the autonomous vehicle is not confused. Schneider said the map of the car itself warns that there is a sign on the road in that place. In addition, these vehicles analyze a huge amount of data to operate under varied weather conditions such as snow, rain, frost, and flooding.
Autonomous vehicle companies even use computer-generated situations to train the car for going through situations such as a dazzling sunset. Writing the code for self-driving cars is a complex task. The code must consider various factors such as pedestrians crossing the street, other cars on the road, traffic signs, advertising signs, bicycles, & pedestrians. It’s like managing chaos.
Schneider suggested that one should put themselves in the shoes of the coder to understand these complexities.
For the skeptics, who think of the autonomous car as if it were a castle in the air, it would help a lot to review how far autonomous vehicles have come. In the 80s, the NavLab, a project of Carnegie Mellon University, already equipped vans with computers and sensors to go automatically and assisted. In 1995, the project “Crossing America without hands “, from the same university, made 98% of the journey between Pittsburgh and southern California autonomously, in addition to a stretch of 113 kilometers without any human intervention, said Schneider.
In 2000, the university began working with all-terrain vehicles. The novelties added to the roadsters were the GPS and the Lidiar to facilitate the task of detecting objects and circumventing them. It was also at that time that Google realized the potential of autonomous vehicles and launched its autonomous driving project. Since then, AI, machine learning and deep learning have only improved.
Why will the consumer feel comfortable aboard a self-guided vehicle? According to Uber’s experience, which has been testing autonomous vehicles in Pittsburgh and Phoenix, Schneider said, the public seems to be opening up to them. Although there was a certain fear initially that people would get scared with this type of car, “we found the exact opposite,” he said. For example, since the passenger cannot choose an autonomous Uber car, some users try to get them by requesting services in the expectation of achieving it. However, there is something that could delay the development of autonomous cars and their entry into the mass market: the business model. At the moment, it is even cheaper to have a car than to take Uber everywhere.
The Present and Future of AI in Cars
With rapid advancements in technology, cars are now equipped with safety features & conditioned to perfection. However, researchers aim to take it further by implementing artificial intelligence, which will transform our vehicles into self-reliant entities.
AI has already taken center stage & researchers are striving to develop the best implementation strategy. The incorporation of AI will revolutionize the way we drive and bring an era of unparalleled safety, comfort, & efficiency.
However, the only claim of experts is not to get the autonomous car. Artificial intelligence can be implemented in many other ways to improve our driving through the learning of our routines. As we have already mentioned, the main objective of artificial intelligence is the so-called deep learning, the total autonomy of the car. However, the mastery of emotions, vital in dangerous decisions when we get behind the wheel, is inimitable. Moreover, it is impossible for a device to store all situations that may occur, so it is difficult to create a pattern for such cases.
Toyota to invest 1,000 million dollars
Artificial intelligence is going to be so important in the future that the most farsighted manufacturers are preparing many resources to develop it. Toyota will invest 1,000 million dollars for five years to create a company called Toyota Research Institute (TRI), located in the Silicon Valley, dedicated to deepen these technologies. Around 200 researchers are being recruited for this important project created in collaboration with the University of Sandford and the Massachusetts Institute of Technology.
The TRI will investigate the autonomous driving and behavior patterns of human beings inside and outside the car. Technologies that facilitate driving with special interest in helping the elderly, a growing sector in the main societies, will be sought. Processors will also be created that more accurately differentiate the environment and supercomputers compact enough to be mounted on vehicles accessible to all.
More intelligence, more security
In the field of security, artificial intelligence will allow a car with autonomous driving to better learn its surroundings, calculate risks and act in anticipation of different hazards. You cannot put all the objects, sidewalks, street lamps, buildings or cars parked on every street in the world on a hard disk, but you can now create computers that differentiate objects, living things, and vehicles that your cameras and sensors had not seen before and catalog them in different categories.
Differentiating an animal like a dog from a motorcycle parked on the sidewalk helps the computer calculate if the animal is in motion and the risk of crossing the path of the vehicle. More important is to differentiate a cyclist from a pedestrian regardless of whether they are moving or the size of each one. A car with artificial intelligence can distinguish a sidewalk no matter how high it is, how far it is from the lane or how it is.
The computer constantly learns and understands everything it sees in places it has never been and will react by warning the driver with automatic actions when it is calculated that there is a risk, such as possible accidents or accidents.
Another field that will evolve in the coming years is that related to insurers and intelligent processes. Predicting what is going to happen is essential for a company dedicated to securing our belongings, so all the tools that help assess possible risks are welcome.
For the user, there are also applications that will inform us of what it will cost to fix a car in an accident. In the process, the user will be informed of what it will cost and how much will the insurance cover.
This race towards artificial intelligence in cars has no truce. Until complete autonomous driving is achieved (not expected until the next decade), the improvement of this process is constant. We will wait to see how they face the technological and ethical dilemmas (that human life depends on a machine) and we achieve a transcendental milestone in the history of transport.