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All aeronautics require a triple voting arbitration system where the correct answer is the one voted by the majority. But those are not generally autonomous sy

All aeronautics require a triple voting arbitration system where the correct answer is the one voted by the majority. But those are not generally autonomous systems and if they are they operate within much simpler environments. In the case of multiple sensors and a voting mechanism the pilot is still in control and making the decisions. These are two very different kinds of systems! SV fantasies of playing fast and loose with hopium. Ah, so that’s it. Your arguments are based mainly on emotion and not engineering. You still haven’t explained to me how these systems can deal with the original problem I presented. EDIT: You'll notice that the commenter appealing to the populism of saying snarky things about Silicon Valley is receiving more encouragement from this forum than I am. I am very much trying to keep things even keeled and ChuckNorris is doing very much the opposite. Would everyone prefer that we just shoot emotionally charged hot takes back at each other with a minimum of engineering focused discussion?

solardev 3y ago
(Not the person you were responding to, but) I don't really see the dilemma here. While driving, humans use sight, sound, touch (like rumble strips), and occasionally smell (engine scents, crashes, wildfires) as inputs. Sometimes these inputs conflict, but we make judgment calls. Sometimes those judgment calls are wrong, but I'd still rather have the multiple inputs than not. With a box on the street, cardboard or not, I wouldn't want to drive over it mindlessly. What if there was something sharp and heavy inside the cardboard box? Better to detect its presence and either come to a graceful stop if possible, swerve around it gently if possible, or warn the driver to take action and then ungracefully brake as a last resort. I wouldn't want any system to drive right over a possible collision based on an assumption of its material. If the camera sees an obstacle, the lidar confirms it's not an illusion, the radar senses it too... that doesn't seem like some unresolvable dilemma. That's a triple redundant system telling you there's something potentially dangerous on the street ahead, and a judgment call has to be made. In this case it seems like those systems would more likely agree than not. A more realistic scenario might be when the camera is obstructed due to glare, or the lidar due to fog or whatever and they disagree about the state of things. Then yes some arbiter would have to make a judgment call. Sometimes it'll be wrong. Sometimes deadly wrong. But that's the case whether it had three inputs or two or just one, or or one camera in a stereo setup has dirt on it, or a bird poop on the windshield is misidentified, etc. Being able separate input noise and errors is part of the job of these systems, something that's made easier with different sensor types that can cross check each either.
- williamcotton 3y ago
  > I wouldn't want any system to drive right over a possible collision based on an assumption of its material. Imagine a scenario where it is worse to change lanes or to quickly break than to just drive over the box... Like the time I was on I-95 in heavy, fast moving traffic and I saw a box flutter off the back of a truck and I decided to just let it bounce off my windshield. There are endless amounts of these kinds of problems. It would be useless to attempt to enumerate them, either in writing, or in code, which speaks to the power of neural networks!
  - solardev 3y ago
    It's true, but in that case it's more a matter of prior experience (oh, I bet that cardboard box/squirrel/etc. probably won't hurt me) than resolving conflicting sensors. That probably lives a few levels above the raw sensor inputs anyway. It's an argument for more powerful training, not fewer sensors... those aren't mutually exclusive goals. Isolating it to just the sensor layer, though, there are often legitimate times when a visual spectrum camera alone isn't ideal. Like not being able to maintain distance to the truck ahead because the glare of the sun is bouncing off its reflective back label at just the right (or wrong) angle, or an odd long object is sticking out the back of a pickup and the object detection can't figure out what it is (but a lidar or radar could sense the presence of something). Or for nighttime driving when deer glow in the infrared far enough ahead but not so much in visual. Or driving by another car in "dealer camouflage". Having another type of sensor could be life saving. For what it's worth, that's true for non autonomous systems too. The human sensor suite in drivers is already often augmented by additional sensors, like parking radars or infrared HUDs or or at least an additional pair of stereoscopic visual cameras with their own judgment. I think these are fundamentally different parts of the overall system. Sure, you can train algorithms on one input type alone. Or multiple. But unless there's evidence showing that training on multiple inputs produces worse final outcomes than training on just one alone, it seems that having multiple sensors should be a net positive.
    - williamcotton 3y ago
      But unless there's evidence showing that training on multiple inputs produces worse final outcomes than training on just one alone, it seems that having multiple sensors should be a net positive. Go train a CNN on 4k images and then go train a CNN on those same images at VGA resolution and you'll see just how problematic a larger set of inputs can be for minimal gains! I have never argued that LIDAR couldn't be used by an already trained autonomous system in the same manner that a human driver is presented with LIDAR sensory inputs! An android seems to be the trajectory we're on if we want a machine to have the same embodied decision making that humans are capable of. Like I said in my original comment, this infinity of embodied experiences seems to be software problem, not a sensor problem.

solardev 3y ago

(Not the person you were responding to, but) I don't really see the dilemma here. While driving, humans use sight, sound, touch (like rumble strips), and occasionally smell (engine scents, crashes, wildfires) as inputs. Sometimes these inputs conflict, but we make judgment calls. Sometimes those judgment calls are wrong, but I'd still rather have the multiple inputs than not. With a box on the street, cardboard or not, I wouldn't want to drive over it mindlessly. What if there was something sharp and heavy inside the cardboard box? Better to detect its presence and either come to a graceful stop if possible, swerve around it gently if possible, or warn the driver to take action and then ungracefully brake as a last resort. I wouldn't want any system to drive right over a possible collision based on an assumption of its material. If the camera sees an obstacle, the lidar confirms it's not an illusion, the radar senses it too... that doesn't seem like some unresolvable dilemma. That's a triple redundant system telling you there's something potentially dangerous on the street ahead, and a judgment call has to be made. In this case it seems like those systems would more likely agree than not. A more realistic scenario might be when the camera is obstructed due to glare, or the lidar due to fog or whatever and they disagree about the state of things. Then yes some arbiter would have to make a judgment call. Sometimes it'll be wrong. Sometimes deadly wrong. But that's the case whether it had three inputs or two or just one, or or one camera in a stereo setup has dirt on it, or a bird poop on the windshield is misidentified, etc. Being able separate input noise and errors is part of the job of these systems, something that's made easier with different sensor types that can cross check each either.

williamcotton 3y ago
> I wouldn't want any system to drive right over a possible collision based on an assumption of its material. Imagine a scenario where it is worse to change lanes or to quickly break than to just drive over the box... Like the time I was on I-95 in heavy, fast moving traffic and I saw a box flutter off the back of a truck and I decided to just let it bounce off my windshield. There are endless amounts of these kinds of problems. It would be useless to attempt to enumerate them, either in writing, or in code, which speaks to the power of neural networks!
- solardev 3y ago
  It's true, but in that case it's more a matter of prior experience (oh, I bet that cardboard box/squirrel/etc. probably won't hurt me) than resolving conflicting sensors. That probably lives a few levels above the raw sensor inputs anyway. It's an argument for more powerful training, not fewer sensors... those aren't mutually exclusive goals. Isolating it to just the sensor layer, though, there are often legitimate times when a visual spectrum camera alone isn't ideal. Like not being able to maintain distance to the truck ahead because the glare of the sun is bouncing off its reflective back label at just the right (or wrong) angle, or an odd long object is sticking out the back of a pickup and the object detection can't figure out what it is (but a lidar or radar could sense the presence of something). Or for nighttime driving when deer glow in the infrared far enough ahead but not so much in visual. Or driving by another car in "dealer camouflage". Having another type of sensor could be life saving. For what it's worth, that's true for non autonomous systems too. The human sensor suite in drivers is already often augmented by additional sensors, like parking radars or infrared HUDs or or at least an additional pair of stereoscopic visual cameras with their own judgment. I think these are fundamentally different parts of the overall system. Sure, you can train algorithms on one input type alone. Or multiple. But unless there's evidence showing that training on multiple inputs produces worse final outcomes than training on just one alone, it seems that having multiple sensors should be a net positive.
  - williamcotton 3y ago
    But unless there's evidence showing that training on multiple inputs produces worse final outcomes than training on just one alone, it seems that having multiple sensors should be a net positive. Go train a CNN on 4k images and then go train a CNN on those same images at VGA resolution and you'll see just how problematic a larger set of inputs can be for minimal gains! I have never argued that LIDAR couldn't be used by an already trained autonomous system in the same manner that a human driver is presented with LIDAR sensory inputs! An android seems to be the trajectory we're on if we want a machine to have the same embodied decision making that humans are capable of. Like I said in my original comment, this infinity of embodied experiences seems to be software problem, not a sensor problem.