It also allows us to detect weaker targets from farther away… even in the presence of stronger targets that are closer or in the same range and doppler, while effectively handling interferences. This paradigm shift in architecture enables a veritable leap in performance – increasing the probability of detection, while reducing the clutter of echoes. Moving radar sensors into the digital age, our software-defined digital imaging radar promises to deliver true imaging in 4D, with much higher resolution, dynamic range, and level of accuracy than conventional analog radar. Utilizing the expertise of our parent company, Intel, we’re developing new, more advanced versions of both types of sensors, in a more cost-effective setup, specifically designed for self-driving cars. Mobileye’s innovation, however, runs much deeper than how we process the data from the radar and LiDAR to the essential types of these sensors we’re employing. This method is simultaneously more robust and more streamlined, while offering a key failsafe. That system then produces a single model of the environment, in a method known as “sensor fusion.” Mobileye’s differentiated approach of True Redundancy creates two parallel AV sub-systems, with two independent models of the driving environment – one from cameras, one from radar and LiDAR – each operating independently of the other. Most companies pursuing autonomous-vehicle technology feed information from all three types of sensors – cameras, radars, and LiDARs – into one sensing system. Even at this basic level, however, Mobileye’s approach differs fundamentally from that more commonly practiced across the industry. Both types have been widely embraced across the autonomous-vehicle industry for their unique respective capabilities, each filling in the blind spots left by the other. Radar does this with radio waves, while LiDAR employs infrared light. This allows them to detect other objects and road users and determine their range (or relative distance). That’s why we’re developing our own.ĭifferent from cameras which passively understand their environment, radar and LiDAR both work by actively emitting signals and measuring their returns. But we soon found that the existing detection-and-ranging sensors on the market still leave much to be desired. And we're developing each system – camera and radar/LiDAR – to be able to power the AV in complete autonomy of the other. So, in addition to our camera-only developmental AV, we’ve also been testing another type using only radar and LiDAR. But if autonomous vehicles are to take over complete control from human drivers, they’ll require multiple types of sensors working in parallel. Thunderstorms can also erupt under the high moisture plumes.Mobileye was founded on computer-vision technology, and we’re proud of what we’ve achieved through the combination of simple cameras and advanced algorithms. Water vapor imagery is useful for indicating where heavy rain is possible. The highest humidities will be the whitest areas while dry regions will be dark. WATER VAPOR IMAGERY: Water vapor satellite pictures indicate how much moisture is present in the upper atmosphere (approximately from 15,000 ft to 30,000 ft). The fog product combines two different infrared channels to see fog and low clouds at night, which show up as dark areas on the imagery. Infrared imagery can also be used for identifying fog and low clouds. Strong to severe thunderstorms will normally have very cold tops. Infrared imagery is useful for determining thunderstorm intensity. Clouds will be colder than land and water, so they are easily identified. The sensors also measure heat radiating off the surface of the earth. Instead of using sunlight to reflect off of clouds, the clouds are identified by satellite sensors that measure heat radiating off of them. INFRARED IMAGERY: Infrared satellite pictures show clouds in both day and night. Satellite will see the developing thunderstorms in their earliest stages, before they are detected on radar. Visible imagery is also very useful for seeing thunderstorm clouds building. If the rivers are not visible, they are probably covered with clouds. Rivers will remain dark in the imagery as long as they are not frozen. Snow-covered ground can also be identified by looking for terrain features, such as rivers or lakes. To help differentiate between clouds and snow, looping pictures can be helpful clouds will move while the snow won't. In winter, snow-covered ground will be white, which can make distinguishing clouds more difficult. On these images, clouds show up as white, the ground is normally grey, and water is dark. VISIBLE IMAGERY: Visible satellite pictures can only be viewed during the day, since clouds reflect the light from the sun. There are three main types of satellite images available:
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