One obstacle to launching a fully autonomous car is achieving 100% certainty in the vehicle’s cameras and sensors.
Under every lighting level and weather condition, cameras must reliably “see” pedestrians and other physical objects ― and trigger an appropriate reaction from critical systems such as braking. This is especially challenging in foggy conditions, which can confuse visual cameras, radar, lidar and other conventional sensor technologies. Thermal imaging represents a potential solution, helping sensors get a much more defined view via infrared technology. But how can thermal imaging developers ― and the developers of other sensor systems ― accurately assess their products’ performance in fog? Recently Ansys collaborated with FLIR Systems, a leader in thermal imaging, to evaluate the fog modeling capabilities in its Ansys Speos solution for simulating its sensor system in foggy driving conditions. The tests verified that FLIR’s thermal camera produced accurate images ― and also verified that fog modeling via Ansys Speos predicts and matches physical fog tests in the lab. In the race to commercialize autonomous vehicles, modeling complex environmental conditions such as fog can be a significant competitive edge for perception engineers, ADAS and AV developers.
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