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After looking at the report it looks like Tesla ran into the same issue we did in the 2007 DARPA Urban Challenge. The trailer was higher than the front facing s

After looking at the report it looks like Tesla ran into the same issue we did in the 2007 DARPA Urban Challenge. The trailer was higher than the front facing sensors. We and most other teams had all assumed 'Ground Based Obstacles' meant that any obstacles on the test track would make contact with the ground in the lane of travel. DARPA decided to put a railroad bar across the street and expected cars to back up and do a U-Turn when they encountered it. The bar was too high off the ground for our forward LIDAR to see it so we collided with the bar at nearly full speed.[1] The sad part about this is that when we were drinking after dropping out of the challenge our team leader said something along the lines of 'At least we know no one will ever die now from the mistake we just made.' [1] https://www.wired.com/2007/10/safety-last-for/ https://www.wired.com/2007/10/safety-last-for/

mjevans 10y ago
Predictions like that only work out if the lesson is sufficiently broadcast. Clearly, since this is still newsworthy here, that is not the case. (However this back channel is helping.)
- maxander 10y ago
  If there isn't some compilation or review article along the lines of "all serious failure modes encountered in autonomous vehicles since 2005-ish," there should be.
  - taneq 10y ago
    That's an excellent idea and should be managed centrally (by the NHTSA or similar). Basically a communal regression test suite that all self-driving vehicles have to pass.
- uncoder0 10y ago
  Agreed, this is a fantastic idea. All the DARPA documents from all three autonomous car related Grand Challenges should be availible. Not sure if they are all released on their website if not they're FOIA'able since most everything DARPA does is public.
agumonkey 10y ago
I don't understand why the paradigm isn't about full volumetric prediction .. is it because people were too tired of solving all other issues (surely possible at the time of DARPA challenge, but Tesla had time and money since); or hardware limits (like scanning a larger area would impede signal quality or make processing too heavy ..)
- Piskvorrr 10y ago
  Which one seems more likely? "We could absolutely do that, but meh...just ship it already, who cares" or "doing that would require a major redesign, more sensors and more gigaflops"? (Okay, in the current hw/sw development paradigm, the first one doesn't sound as unlikely as it ought to)
  - agumonkey 10y ago
    For a company like Tesla, I dearly hope they don't think this way. If so it would mean everything they talked about was marketing drivel (the safety of the car frame etc). So far Musk felt honest with most of his design claims. Unlike lone SDV hackers on github for instance.
- bdnsgt 10y ago
  I'm not sure what you mean, precisely. The grand goal IS complete autonomous function, but it's wise to approach this with caution. Legal and moral grey area abound, new technology brings new technophobia, definite "completeness" would require rigorous proof and we're JUST getting autonomous vehicles into the wild. But the kicker is that if you actually care about the safety of the public, then you have to care about whether or not the public will adopt the new technology that makes them safer-- and to do that you have to introduce it slowly and respond to criticism. Other companies are being even more cautious; Ford isn't putting autonomy on the road until 2021. In the meanwhile Tesla can sell products as Level 2, and perhaps should do so until roads are as safe as commercial aircraft.
  - agumonkey 10y ago
    I know and I don't care. If they want public approval they can run demonstration, and probably 392584 other marketing techniques to generate social interest. Crappy solutions suck; period.
greglindahl 10y ago
The main issue in Tesla's case is that "assist" can't prevent an accident if the driver isn't alert for 7 seconds. Tesla's software is level 2; you were aiming at 3+. As Tesla heads for 3+, sure, they're going to have to solve the problem you (literally) ran into.
deleted 10y ago
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theluketaylor 10y ago
Making sure mistakes only happen once is why I think all companies working on self driving cars should have to supply their sensor data for a fixed time before any collision or successful crash avoidance publicly. The changes they make to their code remain their own trade secret, but building up an extensive library of test cases means everyone's software will get safer faster. So far NHTSA has encouraged such data sharing but they haven't outright mandated it.

mjevans 10y ago

Predictions like that only work out if the lesson is sufficiently broadcast. Clearly, since this is still newsworthy here, that is not the case. (However this back channel is helping.)

maxander 10y ago
If there isn't some compilation or review article along the lines of "all serious failure modes encountered in autonomous vehicles since 2005-ish," there should be.
- taneq 10y ago
  That's an excellent idea and should be managed centrally (by the NHTSA or similar). Basically a communal regression test suite that all self-driving vehicles have to pass.
uncoder0 10y ago
Agreed, this is a fantastic idea. All the DARPA documents from all three autonomous car related Grand Challenges should be availible. Not sure if they are all released on their website if not they're FOIA'able since most everything DARPA does is public.

agumonkey 10y ago

I don't understand why the paradigm isn't about full volumetric prediction .. is it because people were too tired of solving all other issues (surely possible at the time of DARPA challenge, but Tesla had time and money since); or hardware limits (like scanning a larger area would impede signal quality or make processing too heavy ..)

Piskvorrr 10y ago
Which one seems more likely? "We could absolutely do that, but meh...just ship it already, who cares" or "doing that would require a major redesign, more sensors and more gigaflops"? (Okay, in the current hw/sw development paradigm, the first one doesn't sound as unlikely as it ought to)
- agumonkey 10y ago
  For a company like Tesla, I dearly hope they don't think this way. If so it would mean everything they talked about was marketing drivel (the safety of the car frame etc). So far Musk felt honest with most of his design claims. Unlike lone SDV hackers on github for instance.
bdnsgt 10y ago
I'm not sure what you mean, precisely. The grand goal IS complete autonomous function, but it's wise to approach this with caution. Legal and moral grey area abound, new technology brings new technophobia, definite "completeness" would require rigorous proof and we're JUST getting autonomous vehicles into the wild. But the kicker is that if you actually care about the safety of the public, then you have to care about whether or not the public will adopt the new technology that makes them safer-- and to do that you have to introduce it slowly and respond to criticism. Other companies are being even more cautious; Ford isn't putting autonomy on the road until 2021. In the meanwhile Tesla can sell products as Level 2, and perhaps should do so until roads are as safe as commercial aircraft.
- agumonkey 10y ago
  I know and I don't care. If they want public approval they can run demonstration, and probably 392584 other marketing techniques to generate social interest. Crappy solutions suck; period.

greglindahl 10y ago

The main issue in Tesla's case is that "assist" can't prevent an accident if the driver isn't alert for 7 seconds. Tesla's software is level 2; you were aiming at 3+. As Tesla heads for 3+, sure, they're going to have to solve the problem you (literally) ran into.

deleted 10y ago

[deleted]

theluketaylor 10y ago

Making sure mistakes only happen once is why I think all companies working on self driving cars should have to supply their sensor data for a fixed time before any collision or successful crash avoidance publicly. The changes they make to their code remain their own trade secret, but building up an extensive library of test cases means everyone's software will get safer faster. So far NHTSA has encouraged such data sharing but they haven't outright mandated it.