Artificial intelligence (AI) and self-driving cars are often complimentary topics in technology. Simply put, you cannot really discuss one without the other.
Autonomous vehicles (AV) are equipped with multiple sensors, such as cameras, radars and lidar, which help them better understand the surroundings and in path planning. These sensors generate a massive amount of data. To make sense of the data produced by these sensors, AVs need supercomputer-like, nearly instant processing capabilities. Companies developing AV systems rely heavily on AI, in the form of machine learning and deep learning, to process the vast amount of data efficiently and to train and validate their autonomous driving systems.
The first use of AI for autonomous driving goes back to the second Defense Advanced Research Projects Agency (DARPA) Autonomous Vehicle Challenge in 2005, which was won by the Stanford University Racing Team's autonomous robotic car 'Stanley'. The winning team, led by Sebastian Thurn, an associate professor of computer science and director of Stanford Artificial Intelligence Laboratory, attributed the victory to use of machine learning. Stanley was equipped with multiple sensors and backed by custom-written software, including machine learning algorithms, which helped the vehicle find the path, detect obstacles and avoid them while staying on the course.
Artificial intelligence powers self-driving vehicle frameworks. Engineers of self-driving vehicles utilize immense information from image recognition systems, alongside AI and neural networks, to assemble frameworks that can drive self-sufficiently. The neural networks distinguish patterns in the data, which is fed to the AI calculations. That data includes images from cameras for self-driving vehicles. The neural networks figure out how to recognize traffic signals, trees, checks, people on foot, road signs, and different pieces of any random driving environment.
“The autonomous vehicle segment is the fastest growing segment in the automotive industry. Artificial Intelligence is indeed the most important and sophisticated component of self driving vehicles” (Carmody, Thomas, 2019).
The challenges in developing AI systems for something as complex as a self driving vehicle are many. The AI has to interact with multitude of sensors and has to use data in real time. Many AI algorithms are computationally intensive and are therefore hard to use with CPUs that have memory and speed restrictions. Modern vehicles are an example of real time systems that have to produce deterministic results in the time domain. This is related to achieving safety while driving the vehicle. Complicated distributed systems like these require a lot of internal communications that are prone to latency which can disturb the decision making of the AI algorithms. In addition there is the issue of power consumption of the software running in the car. The more intensive AI algorithms consume more power, which is an issue, especially for electric vehicles that depend only on the charge of the battery (Carmody, Thomas, 2019).
AI is used for several important tasks in a self driving automobile. One of the main tasks is path planning. That is the navigation system of the vehicle (Sagar and Nanjundeswaraswamy, 2019). Another big task for AI is the interaction with the sensory system and the interpretation of the data coming out of sensors.
Google has also started to develop self-driving cars, which use a mix of sensors, light detectors, and technologies like GPS and cameras. The following are some basic instructions on how a google car works:
The driver sets a destination. The vehicle’s software predicts and ascertains a course.
A turning, rooftop-mounted Lidar sensor screens a 60-meter range around the vehicle and makes a dynamic three-dimensional (3D) guide of the vehicle’s present environment.
A sensor on the left back tire screens sideways development to identify the vehicle’s position comparative with the 3D guide.
Radar frameworks toward the front and back bumpers ascertain distances to obstacles.
Artificial intelligence programming in the vehicle is associated with every one of the sensors and gathers data from Google Street View and camcorders inside the vehicle.
The AI recreates human perceptual and dynamic cycles utilizing deep learning algorithms and controls activities in driver control frameworks, like steering and brakes.
The vehicle’s software counsels Google Maps for early notification of things like tourist spots, traffic signs and lights and other obstacles
An override function is accessible to enable a human to take responsibility for the vehicle.
Autonomous vehicles are starting to become a real possibility in some parts of industry. (Agriculture, transportation and military are some of the examples.) The day when we are going to see autonomous vehicles in everyday life for the regular consumer is quickly approaching.
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