CIRTEMO Blog CIRTEMOpavessaferroadforautonomousdrivingsystemswithMultivariateOpticalComputing

LIDAR, RADAR, and cameras work well for detecting and providing distance information for most autonomous vehicle applications; however, they cannot discern chemical signature information and/or determine whether an object is living. Hyperspectral Imaging (HSI) systems work well for providing chemical information about a scene; however, they are not ideally suited for autonomous driving applications. Most HSI systems are very expensive, complicated, delicate and generate tremendous amounts of raw data. The size of analytical data has a direct impact on the data transmission (or bandwidth), processing, and storage requirements for an intended sensor; thus, traditional HSI systems cannot process the raw data fast enough to provide real-time data for decision making for autonomous driving sensors and systems applications.CIRTEMO is developing an advanced, real-time obstacle detection/classification sensor with superior spectroscopic imaging performance while minimizing size, weight, power and cost (SWaPc) for the detection of specific chemical signatures (eg. skin, plastics, foliage, etc.). The enabling technology component is the Multivariate Optical Element (MOE) which employs the compressed sensing strengths of spectroscopy into a compact, yet powerful optical sensor.We recently submitted a proposal for funding to the National Science foundation to advance our platform. Our proposal was enthusiastically supported by leading automotive and sensors companies. We are also pursuing commercial partners to expedite our technical efforts. If you and/or you know folks developing autonomous driving systems who may have an interest, drop us a line...info@cirtemo.com.