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View Poll Results: Will self driving autos kill car insurance?
Of course 42 16.60%
Maybe but not for a long time 182 71.94%
I'm a luddite... 29 11.46%
Voters: 253. You may not vote on this poll

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  #2051  
Old 06-06-2018, 04:24 PM
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Sredni Vashtar Sredni Vashtar is offline
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Originally Posted by snakeroberts View Post
I am betting on GM
GM is saying they want to mass-produce a steering-wheel-less car next year. That would be sweet, but who knows if it will actually happen. They only have 100k training miles. They are nowhere near AV, though they are pushing hard right now.

Google, on the other hand, has 7 millions of miles of testing, and just ordered 62k minivans and 20k jags. I doubt they're gonna resell those cars or hire 82k safety drivers. It's time for them to go big.

FWIW, I'm still rooting for tesla-- though their progress has been awful. I want my auto-model 3!
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Last edited by Sredni Vashtar; 06-07-2018 at 12:24 PM..
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  #2052  
Old 06-07-2018, 01:48 PM
snakeroberts snakeroberts is offline
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Quote:
Originally Posted by Sredni Vashtar View Post
GM is saying they want to mass-produce a steering-wheel-less car next year. That would be sweet, but who knows if it will actually happen. They only have 100k training miles. They are nowhere near AV, though they are pushing hard right now.

Google, on the other hand, has 7 millions of miles of testing, and just ordered 62k minivans and 20k jags. I doubt they're gonna resell those cars or hire 82k safety drivers. It's time for them to go big.

FWIW, I'm still rooting for tesla-- though their progress has been awful. I want my auto-model 3!
GM has the best 3rd party mapping and chip companies working with/for them.
GM has tested in harder environments, and those numbers will get closer, plus they can actually build cars. I would not bet against Waymo nor Tesla but I think GM has an edge.
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  #2053  
Old 06-07-2018, 04:59 PM
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I will take the "over" on GM mass-producing a steering-wheel-less car in 2019.

Let's define "mass produce" as meaning that at least 5,000 units have rolled off the assembly line by midnight on 12/31/2019. Reasonable?

ETA: I should add the additional caveat that these cars have to actually be reasonably safe to operate on regular roads. I mean, if GM makes them but they don't work... that doesn't count.
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  #2054  
Old 06-08-2018, 10:10 AM
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The NTSB release a preliminary report on the Tesla crash in California:

https://www.ntsb.gov/investigations/...reliminary.pdf

Quote:
  • The Autopilot system was engaged on four separate occasions during the 32-minute trip, including a continuous operation for the last 18 minutes 55 seconds prior to the crash.
  • During the 18-minute 55-second segment, the vehicle provided two visual alerts and one auditory alert for the driver to place his hands on the steering wheel. These alerts were made more than 15 minutes prior to the crash.
  • During the 60 seconds prior to the crash, the driver’s hands were detected on the steering wheel on three separate occasions, for a total of 34 seconds; for the last 6 seconds prior to the crash, the vehicle did not detect the driver’s hands on the steering wheel.
  • At 8 seconds prior to the crash, the Tesla was following a lead vehicle and was traveling about 65 mph.
  • At 7 seconds prior to the crash, the Tesla began a left steering movement while following a lead vehicle.
  • At 4 seconds prior to the crash, the Tesla was no longer following a lead vehicle.
  • At 3 seconds prior to the crash and up to the time of impact with the crash attenuator, the Tesla’s speed increased from 62 to 70.8 mph, with no precrash braking or evasive steering movement detected.
The driver wasn't paying attention, and it's not clear why the Tesla drifted off lane, but once the car entered the offramp divider, it accelerated into the wall.
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  #2055  
Old 06-08-2018, 11:07 AM
CuriousGeorge CuriousGeorge is offline
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Originally Posted by examsarehard View Post
The NTSB release a preliminary report on the Tesla crash in California:

https://www.ntsb.gov/investigations/...reliminary.pdf



The driver wasn't paying attention, and it's not clear why the Tesla drifted off lane, but once the car entered the offramp divider, it accelerated into the wall.
It's pretty clear why. The actual lane lines are extremely faded there, and there are temporary lines (no longer valid) that are clearly painted. The Tesla got mixed up on which one to follow.

And once it was steering out of the proper lane of traffic, it no longer had a car going 65 in front of it, so it accelerated toward the speed that the driver had set on the cruise control.

Poorly marked lines and stationary items getting filtered from the obstacle assessment are real issues that need to be solved before we can use this technology without having to still pay attention to the road. But why the events caused the accident are pretty well-known.
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  #2056  
Old 06-11-2018, 09:23 AM
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Thanks. That information wasn't in the preliminary report, but it was easy to dig around for other information related to the crash. It seems that the sensors installed on the Tesla are incapable of dealing with stationary objects on the highway, which is why other solutions resort to LIDAR.
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  #2057  
Old 06-11-2018, 10:04 AM
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Originally Posted by CuriousGeorge View Post
It's pretty clear why. The actual lane lines are extremely faded there, and there are temporary lines (no longer valid) that are clearly painted. The Tesla got mixed up on which one to follow.

And once it was steering out of the proper lane of traffic, it no longer had a car going 65 in front of it, so it accelerated toward the speed that the driver had set on the cruise control.

Poorly marked lines and stationary items getting filtered from the obstacle assessment are real issues that need to be solved before we can use this technology without having to still pay attention to the road. But why the events caused the accident are pretty well-known.
Wow. Thanks for the additional information and yeah... I'm feeling pretty good about all of my "over" bets.
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  #2058  
Old 06-11-2018, 10:29 AM
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Originally Posted by twig93 View Post
my "over" bets.
Of the 3, Tesla has the longest odds. They have the cheapest hardware and have not spent a lot of resources on testing.

What they have going for themselves is that *if* they are successful, say next year or the year after, then they could instantly have a million autonomous cars on the road.

My "under" bets have been on Google, which hasn't been in the news as much, since they aren't getting into lethal accidents.
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His enemies called for peace, but he brought them death.
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Last edited by Sredni Vashtar; 06-11-2018 at 10:56 AM..
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  #2059  
Old 06-22-2018, 08:47 AM
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New info on old news. Uber driver in Arizona crash might be charged with vehicular manslaughter. Evidence suggests she was watching The Voice on Hulu.

Quote:
According to the report, Vasquez could face charges of vehicle manslaughter. Police said that, based on testing, the crash was “deemed entirely avoidable” if Vasquez had been paying attention.

Police obtained records from Hulu, an online service for streaming television shows and movies, which showed Vasquez’s account was playing the television talent show “The Voice” the night of the crash for about 42 minutes, ending at 9:59 p.m., which “coincides with the approximate time of the collision,” the report says

Police said a review of video from inside the car showed Vasquez was looking down during the trip, and her face “appears to react and show a smirk or laugh at various points during the times that she is looking down.” The report found that Vasquez “was distracted and looking down” for close to seven of the nearly 22 minutes prior to the collision.
https://www.reuters.com/article/us-g...-idUSKBN1JI04Z
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  #2060  
Old 07-10-2018, 02:23 PM
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https://www.wired.com/story/why-did-...m_medium=email

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WHY DID THE HUMAN CROSS THE ROAD? TO CONFUSE THE SELF-DRIVING CAR

Spoiler:
DRIVING IN A busy city, you have to get good at scrutinizing the body language of pedestrians. Your foot hovers somewhere between the gas and the brake, waiting for your brain to triangulate their intent: Is that one trying to cross the street, or just waiting for the bus? Still, a whole lot of the time you hit the brakes for nothing, ending up in a kind of dance with the pedestrian (you go, no you go, no YOU go).

If you think that’s frustrating, then you’ve never been a self-driving car. As human drivers slowly go extinct (and human pedestrians don’t), autonomous vehicles will have to get better at decoding those unspoken intersection interactions. So a startup called Perceptive Automata is tackling that looming problem. The company says its computer vision system can scrutinize a pedestrian to determine not only their awareness of an oncoming car, but their intent—that is, using body language to predict behavior.

Typically if you want a machine to recognize something like trees, you first have humans label tens of thousands of pictures: trees or not trees. It’s a nice, neat binary. It gives the machine learning algorithms a base level of knowledge. But detecting human body language is more complex.

“In the case of a pedestrian, it's not, this person is crossing the road and this person isn't crossing the road. It's, this person isn't crossing the road but they clearly want to,” says Sam Anthony, co-founder of Perceptive Automata. Is the person looking down the road at oncoming traffic? If they’ve got grocery bags, have they set them down to wait, or are they mid-hoist, getting ready to cross?

Perceptive trains its models to look at those kinds of behaviors. They begin with human trainers, who watch and analyze videos of different pedestrians. Perceptive will take a clip of, say, a human looking down the street to cross the road, and manipulate it hundreds of ways—obscuring portions of it, for instance. Maybe sometimes the head is easier to see, maybe sometimes it’s harder. Then they depart from the tree-not-tree binary by asking the trainers a range of questions, such as, "Is that pedestrian hoping to eventually cross the street?" or “If you were that cyclist, would you be trying to stop the car from passing?”

When different parts of the image are harder to see, the human trainers have to think harder about their judgements of body language, which Perceptive can measure by tracking eye movement and hesitation. Maybe the head is harder to make out, for example, and the trainer has to put more thought into it. “This tells us that there's information about the appearance of the person's head in this particular slice that's an important part of how people judge whether that person in that training video is going to cross the street,” Anthony says.

The head is clearly an important clue for human observers, so it’s also an important clue for the machines. “So when the model saw a novel image where the head was important,” Anthony says, “it would be primed based on the training data to believe that people would likely really care about the pixels around the head area, and would produce an output that captured that human intuition.”

By considering cues like where the pedestrian is looking, Perceptive can quantify awareness and intent. A person walking down the sidewalk with their back to the car, for example, isn’t anything to worry about—both unaware and not intending to cross the street. But someone standing at a crosswalk peering down the street is another story. This insight would give a self-driving car extra time to slow down in case the pedestrian does decide to make a run for it.

Perceptive says it’s already working with automakers—it won’t reveal which—to deploy the system, and plans to license the technology to the makers of self-driving cars. (Daimler, for its part, has also studied tracking pedestrian head movements.) It’s also interested in other robotics companies producing machines that will need to interact closely with humans.

Because in this strange new world of complex interactions between people and robots, it’s as much about machines adapting to humans as it is humans adapting to machines. Determining the intent of pedestrians will help, but it won’t be easy. “Knowing the intent of pedestrians would certainly make [autonomous vehicle] deployment safer,” says Carnegie Mellon roboticist Raj Rajkumar, who works in self-driving cars. “It is, however, a very difficult problem to solve perfectly.”

“Consider Manhattan,” Rajkumar adds. And consider a big group of people crossing, specifically a person on the far side of a group from a robocar. “Among this group, one person is either short or starts running to cross quickly after the vehicle has decided to make a turn. Machine vision is not perfect.” And machine vision can get confused by optics, just like humans can. Reflections, the sun dropping low on the horizon, alternating light and dark patches on the road, not to mention heavy rain or snow, all can bamboozle the machines.

Then there’s the simple matter of people just acting weird. Perceptive’s system can pick up on tell-tale cues, but humans aren’t always so consistent. “There were about 7,000 pedestrian fatalities in the US in 2017 alone,” says Rajkumar. “The primary issue is the presence of significant uncertainty and sudden decisions that get made. Most pedestrians are very traffic-conscious most of the time. But, occasionally, a pedestrian is either in a hurry or changes their mind at the last moment and starts crossing the street, or even reverses direction.”

No one’s about to claim that self-driving cars will totally eliminate traffic deaths—not even machines are perfect, and there’s always going to be the unpredictable human pedestrian element. But bit by bit, robocars are getting better at navigating both our world and our vagaries.


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