JBee
Well-known member
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- JB
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In the moose in the fog scenario radar detecting it first would simply result in the vehicle slowing earlier in the fog, until vision confirms it. It's not a either or situation if vision at first needs to know if any of its data is valid at all whilst driving in fog or heavy rain.I think there is a big problem with combining different types of sensors. I think most of us would agree camera vision is better at sensing the road environment (signs, stoplights, lane lines, etc.), and lidar / radar could exceed camera vision in adverse visual conditions (bad weather). But you have to remember that lidar and radar generate massive amounts of data, and the data is absolutely filled with noise. That noise has to be interpreted with something akin to the AI that is currently interpreting Tesla's visual camera data. It's just a huge extra computing cost to add, and even more likely for the AI to misinterpret that data.
And what would be the real benefit of adding lidar/radar to the current camera vision? Maybe the camera doesn't detect a moose crossing the road in the distance through fog, but the radar does. In this situation the computer/AI needs to decide which sensor to trust. If there's really a moose there, we'd be happy for it to trust the radar and not the cameras. But if there isn't really a moose there, and the radar was picking up a bunch of leaves blowing across the road, you'd be pissed for the car to slam on the brakes and potentially cause an accident.
So it's not as simple as "more sensors leads to better capabilities." Rather, "more sensors leads to more noise, and more ambiguous conflicts for the AI".
It took me a while to get there, but I'm firmly in the visual-only camp.
Rather its a case that Vision would be debiased depending on conditions, and likewise radar when vision is good.
The other item is the consequence of that data, in that an appropriate response can just be slow down until vision confirms brake etc. Slowing down generally has little ramifications if done in a controlled fashion and using rear vehicle distance information to do so. Don't forget vision and radar can track other vehicle speed and trajectory as well, meaning a calulation can run determining the chance of a rear ender as well as any side or foward impacts. This essentially puts you in a 360 safety bubble.
In general this "slowing down according to conditions" is a valid metric and something most sensible drivers do too. It also helps vision and radar to take more responsive actions with a greater safety margin for all vehicles involved.
From a risk management perspective its important to implement the right controls to mitigate risk in all modes and situations, by carefully filtering controls according to the full set of consequences, intended or not. Sometimes this means a control is implemented before approaching a high risk consequence and in this case would still make radar a valid supplement to the system.
If they had to stop car production because of the lack of radar components is however a completely different set of variables and how that was sold to customers something else again.
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