6 ms·

Right, I fail to see how an obstacle as large as a divider is not seen in time. If this is the case, then the system is inadequate to see many types of debris t

Right, I fail to see how an obstacle as large as a divider is not seen in time. If this is the case, then the system is inadequate to see many types of debris that could be on the road.

mirimir 9y ago
Well, it also apparently can't detect stationary semi trailers or fire trucks. So expecting it to detect a little gore point is iffy. But why can't it?
- mdorazio 9y ago
  It's mostly due to limited sensors and immature algorithms. If you think about a sharp left turn with a wall on the right side of the road, there is a "stationary object" (the wall) in front of the vehicle throughout the turn that will be detected by radar and cameras. In order to navigate a situation like this, an autonomous vehicle has to have additional decision-making in place to effectively override "stationary object in front of me = stop" logic. But once you do that, it gets tricky to determine when you should ignore the "stationary object" and when you shouldn't. On top of this, Tesla opted not to include LIDAR as a sensor in its vehicles, so it does not get a 3D point cloud to help identify what an object is and what to do with it. Instead, there's a high reliance on the cameras and radar, but these have some limitations. For example, it's difficult for a camera to distinguish between two objects of similar brightness and color, so for example if there's a white semi-trailer in front of a white wall 100ft away, the camera might not "see" the trailer. This can create situations where sensors are reporting conflicting data, which is bad - for example in the trailer situation, the camera would be saying "there's nothing in front of me until a wall 100ft away", but the radar would be saying "no there's something right in front of me". Or the radar might not "see" an obstacle that is a few feet off the ground while the camera is saying "there's something in front of me". Resolving these conflicts is hard.
  - mirimir 9y ago
    I can see how cameras are basically useless. And also radar, because it just reports relative velocities. And without angular resolution, how could the system distinguish stationary objects on the sides from those straight ahead? Maybe there could be multiple highly directional radar "guns", operating at different frequencies.
  - Dylan16807 9y ago
    > But once you do that, it gets tricky to determine when you should ignore the "stationary object" and when you shouldn't. That's not tricky. You never ignore it. A stationary object means that you will have to either stop or turn. You don't know which one yet, but you definitely know you'll have to take an action. If you haven't decided by some close point, you just pick one.
    - mirimir 9y ago
      As I understand it, what's hard is determining whether that stationary object is directly in front, or off to the side. Cars are always passing stationary objects on the sides.
      - Dylan16807 9y ago
        That doesn't really change my answer. If an object is almost directly in front of you, you either fine-tune your turning to avoid it, or you stop. There's no reason to ever ignore it.
        mirimir 9y ago
        As I understand it, Tesla autopilot can't distinguish objects on the sides (such as road signs) from objects directly in front. And it must ignore road signs etc. So it ends up ignoring all stationary objects.
        Vik1ng 9y ago
        You can't just slam the brakes on the highway. Just a question of time until you get rear ended.
        Piskvorrr 9y ago
        There are other ways to stop than "apply full braking power until speed == 0".
      - Piskvorrr 9y ago
- mtgx 9y ago
  I think the reason is obvious: the hardware and software tech is just not there yet. The hardware that may be actually required to make this work well may still cost tens of thousands of dollars, which means car makers will avoid using it and will use cheaper options like cameras and radar. While on the software side, we may be 80% of the road there, but the other 20% and solving every single edge case out of potentially millions will take another decade or more. And that's without even mentioning software bugs that could make the whole system crash. I think someone mentioned recently that there are on average 15 bugs every 1,000 lines or code or something. Either way, we should expect a lot of bugs to be in this software. Remember Toyota's "spaghetti code"? And now we're just going to assume that Toyota's self-driving cars are going to drive us safely within 3 years? And of course nobody wants to talk about how these cars will be hacked remotely.
  - Piskvorrr 9y ago
    Yup. "80% success" is seen as "It Works Great" by the same technooptimists who would throw a hissy fit over a network service with a mere 80% SLA.
  - oldgradstudent 9y ago
    And even if the "software part" is perfect, the machine learning part is still a black box. You don't know when will it fail or how will it fail.
- Symmetry 9y ago
  To simplify, radars can see how far away objects are and how fast they're moving but only dimly in what direction they are. So if you don't want your car to brake every time you pass under an overhead sign or over a metal grate you need to filter out non-moving returns to some extent. At a certain distance the cone of the radar should only be intersecting things that are actually a danger and you can start braking. But it's really good at noticing if a moving object has changed speed, like the car in front of the car in front of you braking. On Teslas theoretically the camera is there to prevent stuff like this but vision AI isn't all there yet. Other companies use lidar which gives pretty accurate distance and direction but not object speed. Pretty useful for avoiding obstacles but current automotive grade lidars are very expensive. Much cheaper solid state lidars seem to be imminent.

Right, I fail to see how an obstacle as large as a divider is not seen in time. If this is the case, then the system is inadequate to see many types of debris that could be on the road.

mirimir 9y ago

Well, it also apparently can't detect stationary semi trailers or fire trucks. So expecting it to detect a little gore point is iffy. But why can't it?

mdorazio 9y ago
It's mostly due to limited sensors and immature algorithms. If you think about a sharp left turn with a wall on the right side of the road, there is a "stationary object" (the wall) in front of the vehicle throughout the turn that will be detected by radar and cameras. In order to navigate a situation like this, an autonomous vehicle has to have additional decision-making in place to effectively override "stationary object in front of me = stop" logic. But once you do that, it gets tricky to determine when you should ignore the "stationary object" and when you shouldn't. On top of this, Tesla opted not to include LIDAR as a sensor in its vehicles, so it does not get a 3D point cloud to help identify what an object is and what to do with it. Instead, there's a high reliance on the cameras and radar, but these have some limitations. For example, it's difficult for a camera to distinguish between two objects of similar brightness and color, so for example if there's a white semi-trailer in front of a white wall 100ft away, the camera might not "see" the trailer. This can create situations where sensors are reporting conflicting data, which is bad - for example in the trailer situation, the camera would be saying "there's nothing in front of me until a wall 100ft away", but the radar would be saying "no there's something right in front of me". Or the radar might not "see" an obstacle that is a few feet off the ground while the camera is saying "there's something in front of me". Resolving these conflicts is hard.
- mirimir 9y ago
  I can see how cameras are basically useless. And also radar, because it just reports relative velocities. And without angular resolution, how could the system distinguish stationary objects on the sides from those straight ahead? Maybe there could be multiple highly directional radar "guns", operating at different frequencies.
- Dylan16807 9y ago
  > But once you do that, it gets tricky to determine when you should ignore the "stationary object" and when you shouldn't. That's not tricky. You never ignore it. A stationary object means that you will have to either stop or turn. You don't know which one yet, but you definitely know you'll have to take an action. If you haven't decided by some close point, you just pick one.
  - mirimir 9y ago
    As I understand it, what's hard is determining whether that stationary object is directly in front, or off to the side. Cars are always passing stationary objects on the sides.
    - Dylan16807 9y ago
      That doesn't really change my answer. If an object is almost directly in front of you, you either fine-tune your turning to avoid it, or you stop. There's no reason to ever ignore it.
      - mirimir 9y ago
        As I understand it, Tesla autopilot can't distinguish objects on the sides (such as road signs) from objects directly in front. And it must ignore road signs etc. So it ends up ignoring all stationary objects.
      - Vik1ng 9y ago
        You can't just slam the brakes on the highway. Just a question of time until you get rear ended.
        Piskvorrr 9y ago
        There are other ways to stop than "apply full braking power until speed == 0".
    - Piskvorrr 9y ago
mtgx 9y ago
I think the reason is obvious: the hardware and software tech is just not there yet. The hardware that may be actually required to make this work well may still cost tens of thousands of dollars, which means car makers will avoid using it and will use cheaper options like cameras and radar. While on the software side, we may be 80% of the road there, but the other 20% and solving every single edge case out of potentially millions will take another decade or more. And that's without even mentioning software bugs that could make the whole system crash. I think someone mentioned recently that there are on average 15 bugs every 1,000 lines or code or something. Either way, we should expect a lot of bugs to be in this software. Remember Toyota's "spaghetti code"? And now we're just going to assume that Toyota's self-driving cars are going to drive us safely within 3 years? And of course nobody wants to talk about how these cars will be hacked remotely.
- Piskvorrr 9y ago
  Yup. "80% success" is seen as "It Works Great" by the same technooptimists who would throw a hissy fit over a network service with a mere 80% SLA.
- oldgradstudent 9y ago
  And even if the "software part" is perfect, the machine learning part is still a black box. You don't know when will it fail or how will it fail.
Symmetry 9y ago
To simplify, radars can see how far away objects are and how fast they're moving but only dimly in what direction they are. So if you don't want your car to brake every time you pass under an overhead sign or over a metal grate you need to filter out non-moving returns to some extent. At a certain distance the cone of the radar should only be intersecting things that are actually a danger and you can start braking. But it's really good at noticing if a moving object has changed speed, like the car in front of the car in front of you braking. On Teslas theoretically the camera is there to prevent stuff like this but vision AI isn't all there yet. Other companies use lidar which gives pretty accurate distance and direction but not object speed. Pretty useful for avoiding obstacles but current automotive grade lidars are very expensive. Much cheaper solid state lidars seem to be imminent.