Very sad. I once designed a robot that used infra-red collision detection to avoid obstacles, my home built, and painted flat black, stereo cabinet registered as open space and the robot drove right into it at full speed. It was a quick lesson on sensor fallibility for me.
I've also observed human pilots on busy roads nearly colliding with obstacles when driving toward the setting sun, the visor pulled down and still trying to shade their eyes.
But robots aren't humans, and they don't have to rely only on vision, there are so many ways to "look" it seems like we should have several different ways of identifying obstacles. Different spectra at least.
I have had similar experiences working in robotics and I would imagine anyone else who has worked with designing navigation, mapping, or obstacle avoidance for robots would also be well aware of the shortcomings of each type of sensor.
Even in the confines of indoor environments with simple problems like transparent surfaces, specific problematic fabrics, and acoustic panels it is necessary to fuse several types of sensor data to provide reasonably reliable detection of the environment.
I would hope that as sensors and output analysis techniques continue to be developed and costs decrease, creators of autonomous systems would incorporate a wider variety of sensors into their products.
I've also observed human pilots on busy roads nearly colliding with obstacles when driving toward the setting sun, the visor pulled down and still trying to shade their eyes.
But robots aren't humans, and they don't have to rely only on vision, there are so many ways to "look" it seems like we should have several different ways of identifying obstacles. Different spectra at least.