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Nowadays, we hear about autonomous vehicles coming soon however real autonomous driving in real-world situations is years from being a reality. Human drivers must be aware of the conditions in front of them and the interior of a vehicle can offer a fairly static laboratory-like environment to observe. Eyeris was founded in 2013 as a human-centered artificial intelligence (AI) firm, seeks to make driving more secure and comfortable by monitoring conditions in the interior and ensuring that the driver is in control, as well as confirms that the entire environment can perform the crucial job.
The Challenge: Varied Occupants and Sensing Conditions
Although relatively stable when compared to other places the interior of a vehicle can offer a myriad of challenges. The driver could be in the vehicle on their own or a number of other occupants might be inside the vehicle that could be male or female , and differ in size from tiny children all the way to 100-kilogram adults and above. Consider the fact that humans possess many skin tones and may be wearing different clothes and accessories under different temperature and lighting conditions, and this “lab environment” becomes a quite complex experiment. This is before even considering pets from the family to accompany you on the journey and the hamburger wrapper in the back seat , which hasn’t been cleaned since the day before, and a cell phone or two thrown in the seat of the passenger.
The Solution: Sensor Fusion and Data Abundance
Although one system of sensors may be the best in eye tracking, or other advantages in technology however, in the context of the AI software firm Eyeris rather focuses on fusing several sensors that are built into hardware. As such, they partner with a wide range of hardware manufacturers for sensing technologies–including traditional infrared (IR) modern red, green, blue, plus infrared (RGBIR) sensors, thermal imagers, and even radar–to get an overall view of the situation and collaborate with a wide range of processor manufacturers to run AI routines. This sensor fusion, coupled with an enormous amount of data utilized for training, ensures that the interior of a car can be precisely interpreted similar to how humans combine hearing, sight as well as smell and even taste in order to accomplish an intricate task.
Alongside the computing power required to run the AI system, connections between cameras sensors, processing modules for sensor, and other processing hardware in an automobile must also be taken into consideration. For example, Eyeris has used for certain of its designs for reference Maxim’s MAX96706 deserializer for connecting the mobile industrial processor interface (MIPI)-based camera sensors and image sensor modules to the AI processing board, with excellent results. As automotive electronics become more interconnected, reliable ways for handling and abstracting the data transfer must be taken into take into consideration.
The variety of vehicles made makes it necessary to have an efficient system that is easily integrated into cars Z, X, or Z will significantly cut the development cost and time to market.
Hardware Innovation: Facilitating Software Innovation
We’ve seen an astonishing growth in computer power and hardware innovations in the last few decades. However, the software innovation cycles naturally occur at a faster speed than those for hardware, and companies often have to be in “catch-up” mode in relation to their counterparts in the software world. It’s one of the reasons Tesla, Apple, and other companies create its own AI hardware specifically to adapt to the software advancements which are coming up.
For smaller AI/software companies that partner with a diverse variety of hardware manufacturers it is crucial to have software stacks that are mature as well as software development kit (SDKs) compatible with modern AI frameworks like TensorFlow, PyTorch, and ONNX. In addition, they should have adequate computational capabilities. Compilers that are available should support the latest neural network layers that include advanced simulator engines, software emulators and similar tools for AI modeling pruning, quantization, and many other tasks. In addition, enabling sensors fusion functions with built-in 3D disparity engine, streaming across multiple cameras, advanced output and input (IO) interfaces as well as other features are extremely beneficial. This permits AI and the people who create AI systems to operate with a wide range of data, while reducing the chaos.
AI Sensor Fusion: Automotive Safety and More
This blog is focused on the inside of cars generalizedly various applications are in place where a conventional vision-only AI setup could be an appropriate choice, but might not be adequate for a specific application. Particularly for safety-sensitive situations the system that functions mostly under proper lighting and other circumstances may not be adequate. In these situations, adding additional sensing capabilities–whether that be a second RGB visible light device, an IR sensor, radar, or even something like a thermal sensor for enhanced presence detection–may enable AI to sufficiently monitor and control an environment.
Multibillion-dollar businesses might have the funds to create their own chips internally However, in other circumstances smaller, more adaptable AI company could be the ideal choice for the task. In this case, the right hardware partners need to find, develop and integrated to create an all-in-one solution for automobiles and other industries. The more advanced the software and hardware interfacing tools are, the simpler it is to create AI software and the faster the product will be developed. With the correct data, tools and AI training we can create a safer world that is more beneficial for those who use such systems as well as for the entire society.
