Speaker 1: Welcome to tech Stuff, a production of I Heart Radios
Speaker 1: How Stuff Works. Hey there, and welcome to tech Stuff.
Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer with
Speaker 1: I Heart Radio and I love all things tech. And
Speaker 1: if you haven't noticed already, yeah, my voice is all
Speaker 1: sorts of jacked up because I'm getting over a cold.
Speaker 1: I apologize for that, but the tech must go on now.
Speaker 1: Just a couple of years ago, the tech world in
Speaker 1: general was pretty optimistic about autonomous cars, and I include
Speaker 1: myself in that group. I remember seeing the remarkable progress
Speaker 1: that had come out from the first DARPA Grand Challenge
Speaker 1: up to about I don't know ten or so, and
Speaker 1: and it seemed like we were just on the verge
Speaker 1: of having fleets of robo taxis at our back and call.
Speaker 1: But now we've gone on for several more years and
Speaker 1: we're still at a point are only a handful of
Speaker 1: companies are conducting limited tests. Plus there have been some
Speaker 1: high profile cases of accidents involving vehicles operating under autonomous
Speaker 1: or semi autonomous modes that ended in tragedy. So in
Speaker 1: this episode, we're going to take a look at autonomous
Speaker 1: cars and where we stand today. Now, let's start with
Speaker 1: I think it helps if we run through the levels
Speaker 1: of autonomy, and not everyone uses these levels to talk
Speaker 1: about autonomy, and to be honest, the barriers between levels
Speaker 1: are a bit fuzzy, and sometimes we're not really able
Speaker 1: to say where we are at as far as levels
Speaker 1: of autonomy. We can look back at previous developments and say,
Speaker 1: all right, well, judging on where we are now, we'd
Speaker 1: say that this falls into level two or level three,
Speaker 1: But it can be a little difficult to see what
Speaker 1: level we are currently in without you know, truly remarkable evidence.
Speaker 1: But in general, this is a useful way to talk
Speaker 1: about how far along we are uh as far as
Speaker 1: getting fully autonomous cars. So technically the levels range from
Speaker 1: zero to five, so that means there are really six levels. However,
Speaker 1: level zero really means there is no autonomy at all.
Speaker 1: So with that type of vehicle, the human driver is
Speaker 1: responsible for all operations of the vehicle. Every driving task
Speaker 1: is handled by the driver alone. So some folks will
Speaker 1: say that they're really just five levels of autonomy. Zero
Speaker 1: would refer to vehicles that really, honestly, they don't exist
Speaker 1: that much anymore. Now you might be thinking but hey,
Speaker 1: Mr smarty Pants podcast person. I drive a car and
Speaker 1: it doesn't have any autonomous vehicle features, but depending upon
Speaker 1: whom you ask, features like power steering or anti lock
Speaker 1: breaks or cruise control and other pretty common features fall
Speaker 1: into the low level autonomous range. It doesn't mean your
Speaker 1: car is at on this, but it has some of
Speaker 1: the components that are identified with this concept of autonomy.
Speaker 1: So most cars today are actually above level zero if
Speaker 1: we go by that definition there level one are higher.
Speaker 1: So level one autonomy would apply to cars where the
Speaker 1: driver still controls the vehicle. The vehicle is still under
Speaker 1: driver control, but the car has some driver assistance features
Speaker 1: like power steering or antilock brakes. The car might have
Speaker 1: what is called an advanced driver Assistance system or a
Speaker 1: d a S, and the word advanced makes it sound
Speaker 1: a bit fancier than it is at this particular level
Speaker 1: of autonomy. The car might have systems that help people steer,
Speaker 1: or it might have systems that help accelerate and or break,
Speaker 1: but the steering and accelerating or steering and breaking can't
Speaker 1: happen simultaneously. Either one or the other can be taken
Speaker 1: over by these systems, but not both. At the same time,
Speaker 1: not with level one. If we get up to level
Speaker 1: to autonomy, then we're talking about partial automation. The A
Speaker 1: d A s on these cars can do stuff like
Speaker 1: control steering and breaking, or steering and accelerating at the
Speaker 1: same time, at least under certain circumstances. But even in
Speaker 1: those cases, the car's driver still remains primarily in control
Speaker 1: of the vehicle. With this level of autonomy, a driver
Speaker 1: would still not remove their hands from the wheel, as
Speaker 1: the car would need the humans participation to you know,
Speaker 1: works safely. So with level to autonomy, you still have
Speaker 1: to have your attention on the road, you still have
Speaker 1: to have your hands on the wheel. It's just that
Speaker 1: the car can occasionally kick in and assist in various scenarios,
Speaker 1: typically in very restricted cases. Now at level three autonomy,
Speaker 1: we're getting up to conditional automation. These cars would still
Speaker 1: require a human driver, but there can be times when
Speaker 1: the car systems can operate the vehicle on its own
Speaker 1: and the driver is essentially a passenger. During those moments.
Speaker 1: The driver is still supposed to monitor the environment. They're
Speaker 1: still supposed to be prepared to take over the car
Speaker 1: should the vehicle indicate it needs to hand over control
Speaker 1: to the person behind the wheel. So ideally there would
Speaker 1: be a system where the car would identify a situation
Speaker 1: in which the driver needs to take over, and then
Speaker 1: well in advance of that situation becoming imminent, it would
Speaker 1: alert the driver to take over control of the car. Uh.
Speaker 1: This is trickier, right. This is harder to do than
Speaker 1: it than to say, because the car would have to
Speaker 1: know far enough in advance to be able to send
Speaker 1: that alert to the driver, and the driver would have
Speaker 1: to be able to respond to that. And while we
Speaker 1: feel like our response time is really fast, in computational terms,
Speaker 1: we are snails. We move super slow. So this is
Speaker 1: actually pretty tricky, especially if you're talking about a dynamic
Speaker 1: situation where things are changing very rapidly. At level three,
Speaker 1: autonomous cars are supposed to uh do this seamlessly, and
Speaker 1: as I said, that is a pretty tricky thing to
Speaker 1: do technically. Most vehicle systems were looking at now, especially
Speaker 1: the ones like Tesla's autopilot, falls somewhere in level three.
Speaker 1: Level four autonomy is at a point where a vehicle
Speaker 1: can automatically operate itself at least under certain conditions, but
Speaker 1: not necessarily all driving conditions, the vehicle would likely include
Speaker 1: the option for a human driver to take over operations,
Speaker 1: but under normal, you know, conditions that the car would
Speaker 1: pretty much drive itself. So with level for autonomy, you
Speaker 1: would have self driving cars that could act as a
Speaker 1: self driving car for most of the time, but also
Speaker 1: allow a human driver to take over if the human
Speaker 1: driver wanted to. UM. Level five autonomy is a fully
Speaker 1: autonomous car. The car can operate itself under all driving conditions,
Speaker 1: So any condition where a human would be driving a car,
Speaker 1: a level five autonomous car should be able to operate
Speaker 1: in that same situation. There may not be any steering
Speaker 1: wheel or any controls at all in a vehicle, meaning
Speaker 1: there's no option for a human driver to take over.
Speaker 1: Now that's not our prerequisite. You can have a level
Speaker 1: five fully autonomous car that would still have controls and
Speaker 1: still allow humans to take over manually if they if
Speaker 1: they chose to do so. It's just that it's an option,
Speaker 1: it's not. It's no longer a mandatory thing to have
Speaker 1: those human based controls with a level five autonomous car.
Speaker 1: UM So we don't have any of these yet, So
Speaker 1: really talking about this is purely in the hypothetical. Arguably,
Speaker 1: we have some that are in the level four range,
Speaker 1: but there will get to that they're under very strict parameters,
Speaker 1: all right, So most experts agree that the versions of
Speaker 1: autonomous cars we've seen so far are mainly in the
Speaker 1: level three and level four categories, uh, creeping more toward
Speaker 1: a firm level four. We're kind of in the early
Speaker 1: stages of that, and there's several tests programs that are
Speaker 1: operating almost as if we're at level five. But there's
Speaker 1: disagreement about whether or not technology is really sophisticate enough
Speaker 1: to warrant us calling any existing vehicle a level four
Speaker 1: or level five autonomous car. And so, while we have
Speaker 1: some examples of cars and I'll talk about a couple
Speaker 1: of them that lack control systems for human drivers, they
Speaker 1: are almost all prototypes and concept vehicles or in very
Speaker 1: limited testing situations. Uh. And so therefore they don't really
Speaker 1: rank as level five autonomous cars because while they lack
Speaker 1: the controls, they cannot operate in every situation an environment
Speaker 1: that humans drive in. So it's it's too early for
Speaker 1: us to talk about deploying cars that have no way
Speaker 1: to hand over control to human driver in all regions
Speaker 1: and in all you know, driving situations. Okay, so let's
Speaker 1: do a very quick rundown on the history of autonomous
Speaker 1: cars up to say, like or so, and to see
Speaker 1: why sub folks like like yours truly we're so bullish
Speaker 1: on the future of autonomous cars. So the history stretches
Speaker 1: back a good long ways, particularly if we're looking at
Speaker 1: stuff like power steering. But that's getting way too granular.
Speaker 1: I'm not going to do that. And the history is
Speaker 1: also really complex, and that involves lots of different disciplines
Speaker 1: converging into the autonomous car form factor. You have stuff
Speaker 1: like robotics, you know, sensored development, artificial intelligence, computational processing, power,
Speaker 1: range finding technology, lots of things that all have to
Speaker 1: come together. And to really dive into the complex history
Speaker 1: of all the technologies that are coming together to make
Speaker 1: autonomous cars possible would require a whole mini series of episodes.
Speaker 1: So we're not going to jump into all of that
Speaker 1: in this one. Instead, I want to focus on things
Speaker 1: like the DARPA challenges that were created in the mid
Speaker 1: two thousand's. The first one was in two thousand four
Speaker 1: and DARPA, as you'll recall, is the research and Development
Speaker 1: Arm of the depart I'm in the defense in the
Speaker 1: United States, so it's technically an organization that funds various
Speaker 1: other groups to do R and D in technologies that
Speaker 1: ultimately stand to benefit the defense of the United States.
Speaker 1: So while there are other uses for those technologies that
Speaker 1: don't directly relate to defense or military systems, that's the
Speaker 1: primary purpose for DARBA. So in two thousand four they
Speaker 1: created this this challenge. They called for teams to build
Speaker 1: or convert vehicles into autonomous cars that were capable of
Speaker 1: navigating a long distance desert course is more than a
Speaker 1: hundred miles long, and there it needs to be no
Speaker 1: human operators, so it could not be remotely controlled, nor
Speaker 1: would there be a driver in the vehicle. And the
Speaker 1: idea was design a car that would be capable of
Speaker 1: traveling a predesignated route from beginning to end. For that
Speaker 1: two thousand four challenge, no team was able to complete
Speaker 1: eat the challenge. Uh cars failed. Some of them went
Speaker 1: off road and got stuck, some of them just got
Speaker 1: confused and stopped. So no one completed it within the
Speaker 1: time frame that DARPA had set. But it's set the
Speaker 1: stage for subsequent competitions. In two thousand five, DARPA held
Speaker 1: another Grand Challenge again with the desert course. This one
Speaker 1: was a hundred thirty two miles long and this time
Speaker 1: five teams were able to complete the route and the
Speaker 1: winning team was from Stanford Race. Stanford University was the
Speaker 1: Stanford Racing team. They clocked the shortest time on the course.
Speaker 1: By shortest time, I'm still talking about a long time.
Speaker 1: It took them six hours fifty three minutes to make
Speaker 1: the one thirty two mile journey. That would mean that
Speaker 1: the average speed for the vehicle when taken across the
Speaker 1: whole course was somewhere around eighteen miles per hour or
Speaker 1: approximately twenty nine kilometers per hour, which isn't exactly tearing
Speaker 1: up the track, but it was still a very impressive achievement.
Speaker 1: I don't want to take away from what they achieved.
Speaker 1: It was incredible, especially for the time, but it's not
Speaker 1: the sort of speed you would look at and think, oh, well,
Speaker 1: this is the replacement for the modern car. The next
Speaker 1: challenge would happen in two thousand seven, and it switched
Speaker 1: things up by requiring teams to design a car capable
Speaker 1: of navigating through a simulated urban environment complete with traffic
Speaker 1: and traffic laws like you know, traffic lights and stop
Speaker 1: signs and simulated pedestrians. It wouldn't be enough to design
Speaker 1: a car that could detect a road and follow it,
Speaker 1: or even a car capable of managing stuff like how
Speaker 1: to how to send torque two different wheels. In order
Speaker 1: to get out of a tricky situation, the cars would
Speaker 1: need advanced collision detection and decision making capabilities. They have
Speaker 1: to obey traffic laws, they'd have to be able to
Speaker 1: adapt to potentially changing situations, the kind of stuff you
Speaker 1: might find if you're driving around a city. So in
Speaker 1: that case, six teams were able to finish the course,
Speaker 1: Stanford Racing would actually take second place that time. They
Speaker 1: clocked in at just under four and a half hours.
Speaker 1: First place went to a group called Tartan Racing from
Speaker 1: Carnegie Melon University and they finished in four hours ten minutes. Now,
Speaker 1: the purpose of these competitions wasn't just to find out
Speaker 1: which groups of smarty pants engineers were able to build
Speaker 1: the best car. It was an attempt to kick start
Speaker 1: serious development in the various fields related to making autonomous
Speaker 1: cars a possibility. Engineers worked on all sorts of different designs.
Speaker 1: Some incorporated lots of optical cameras. Some used lie dar,
Speaker 1: which is a type of laser based range finding technology
Speaker 1: similar to radar. So it works by zapping out a
Speaker 1: laser and then detecting any reflections coming back from that
Speaker 1: laser light. It uses an array of sensors looking for
Speaker 1: any evidence of that laser light coming back to the sensor,
Speaker 1: and then measures the time difference between when the laser
Speaker 1: went out and when it picked up the reflection, and then,
Speaker 1: working with some math, it can figure out how far
Speaker 1: away an obstacle is from the vehicle. Not only that,
Speaker 1: it can also figure out whether or not that obstacle
Speaker 1: is moving, or if it's stationary, or if it's moving
Speaker 1: away from or toward the vehicle. It can figure out
Speaker 1: all that. Uh, and I've talked about that in past episode,
Speaker 1: so I won't get into the whull technical details here,
Speaker 1: but it was one of those key components that's used
Speaker 1: in some, but not all vehicles that are following under
Speaker 1: this autonomous card development. It's interesting because there are lots
Speaker 1: of different companies that are working on autonomous cars. They
Speaker 1: are not all relying on exactly the same technologies to
Speaker 1: achieve that goal. Some of them are much more heavily
Speaker 1: focused on optical cameras. Some of them are more focused
Speaker 1: on things like lidar and other sensors. Some of them
Speaker 1: involve a whole, you know, slew of different technologies that
Speaker 1: are meant to be both uh, you know, primary systems
Speaker 1: and redundant systems. So it's really interesting and it was.
Speaker 1: It was really impressive to see these teams complete the
Speaker 1: Urban Challenge, but again, it didn't immediately make everyone think
Speaker 1: driverless cars would be available right away. The challenges, while impressive,
Speaker 1: didn't compare to what the average human driver deals with
Speaker 1: on a on a regular day. The competition times were
Speaker 1: pretty long. The average speeds were all below fifteen miles
Speaker 1: per hour, so they're all below twenty four kilometers per hour.
Speaker 1: At that speed, it was clear that these vehicles were
Speaker 1: just the earliest incarnations of technologies that would power autonomous
Speaker 1: cars in the future. So they were airing on the
Speaker 1: side of caution, which frankly, you want in the first place.
Speaker 1: You don't want to see a lot of people say
Speaker 1: let's take some chances, when when you're talking about vehicles,
Speaker 1: I mean, they're human lives at stake. Meanwhile, another narrative
Speaker 1: drives home pun intended why a lot of folks got
Speaker 1: really hyped up about autonomous cars. It's also a sobering
Speaker 1: line of thought. And I'm talking, of course, about the
Speaker 1: frequency of fatal car accidents and how many of them
Speaker 1: can be traced back to human error. Now, getting global
Speaker 1: statistics is pretty tough on this, so I'm going to
Speaker 1: focus on the United States because we have a lot
Speaker 1: of organizations in the US that track these kinds of numbers,
Speaker 1: and you can kind of get an idea of how
Speaker 1: big the problem is. So in tween, the National Safety
Speaker 1: Council released a report that stated and estimated forty thousand
Speaker 1: people had died in car accidents in the United States. Uh,
Speaker 1: that actually amounted to a decline of one percent from
Speaker 1: two thousand seventeen. That was when forty one people died.
Speaker 1: Another four and a half million people on top of
Speaker 1: that had become seriously injured in car crashes in Meanwhile,
Speaker 1: the National Highway Traffics say D administration in the United
Speaker 1: States said that of serious car crashes result due to
Speaker 1: human error or dangerous choices. So, in other words, mechanical
Speaker 1: failures only contribute a very small percentage to the overall numbers.
Speaker 1: When it comes to serious car accidents, most serious car
Speaker 1: accidents aren't caused by a tire blowout or you know,
Speaker 1: a car failing in some way. They're caused by humans
Speaker 1: doing something wrong, whether it's totally by accident or someone
Speaker 1: just makes a really bad decision, like they think, oh,
Speaker 1: sure there's no there's no dashed line here, but I'm
Speaker 1: gonna go ahead and try and pass this person on
Speaker 1: this windy rural road because I bet nobody's coming the
Speaker 1: other way. That's what we would call a bad decision,
Speaker 1: So says the tech optimist. If you could create autonomous
Speaker 1: cars that operates safely, you could eliminate the vast majority
Speaker 1: of car crashes and thus fatalities on the road. You
Speaker 1: just remove the human error element, and suddenly you're talking
Speaker 1: about a staggering result, and that is an incredibly powerful motivator.
Speaker 1: Tens of thousands of people wouldn't die each year from
Speaker 1: these car accidents. Millions more would never be injured or
Speaker 1: affected by the tragic loss of a loved one from
Speaker 1: an accident. Then you start moving outward, you go out
Speaker 1: another circle. You think of this as a ripple effect
Speaker 1: and you think, imagine all the contributions those people might
Speaker 1: make in the future that they'll get a chance to
Speaker 1: make because they wouldn't have had this terrible car crash.
Speaker 1: These are things we never would see come to fruition
Speaker 1: if they were to get in a fatal car crash,
Speaker 1: and it becomes this butterfly effect issue. And of course,
Speaker 1: we want to make the roads safer for everyone. Now,
Speaker 1: I'm sure all of you have already hit upon the
Speaker 1: major issue here. The whole concept of people being safer
Speaker 1: in autonomous cars is contingent upon those autonomous cars performing
Speaker 1: better than humans already do and in every type of
Speaker 1: situation in which humans find themselves driving in. If we
Speaker 1: can't get that right, then we haven't made things safer
Speaker 1: at all. All we would have done is shifted the
Speaker 1: cause of the accidents from human error to machine error
Speaker 1: or computer error. So we must be absolutely certain that
Speaker 1: the vehicles we make meet a very high standard if
Speaker 1: our goal is to reduce car accidents. So we have
Speaker 1: to prove that these machines operate better than people do
Speaker 1: in all the different situations people find themselves driving in
Speaker 1: before we can make any sort of declarative statement of
Speaker 1: this is the best way forward. Now, when we come back,
Speaker 1: I'll talk about why this gets super tricky, and talk
Speaker 1: about thought experiments and things, and and also some real
Speaker 1: world scenarios that kind of illustrate why this is harder
Speaker 1: than what it sounds. But first, let's take a quick break.
Speaker 1: Before the break, I positive that a future with autonomous
Speaker 1: cars that all but eliminate fatal car crashes hinge upon
Speaker 1: building driverless vehicles that are much better at driving cars
Speaker 1: than humans are in all situations. Now, we could get
Speaker 1: a bit more lucy goosey here, but doing so brings
Speaker 1: up some tough ethical issues. So, for example, what if
Speaker 1: we knew that machines were better. Right, autonomous cars are
Speaker 1: better than human drivers, but they are by no means perfect.
Speaker 1: So what if we could be certain that autonomous cars,
Speaker 1: if widely adopted, would reduce those fatalities by half, for example,
Speaker 1: but they would still be at fault in the case
Speaker 1: of the other half of fatal car accidents. So let's
Speaker 1: say it's you know, I don't know, and we have
Speaker 1: level four autonomous cars that are pretty reliably level four
Speaker 1: and they are better as a whole than human drivers are.
Speaker 1: So we've seen a vast reduction in vehicles operated by humans.
Speaker 1: And let's even assume that most cars are now controlled
Speaker 1: by computers, but let's also assume they're not perfect now.
Speaker 1: Using the numbers from if humans were still in control,
Speaker 1: we would expect to see another forty thousand fatalities due
Speaker 1: to human error. And I'm just using that number as
Speaker 1: an example. I realized that in reality we'd be talking
Speaker 1: about nine of forty thousand. But now that cars are
Speaker 1: in control, it means half of those accidents are totally prevented.
Speaker 1: But we still see fatal accidents that claim twenty thousand lives.
Speaker 1: On the one hand, we could look at that scenari
Speaker 1: are you and say, based upon what we know from
Speaker 1: past experience, we would have seen many more people die
Speaker 1: in accidents if humans were actually still operating cars. But
Speaker 1: on the other hand, that's all hypothetical, right, I mean,
Speaker 1: we can only know anything based on what actually happened,
Speaker 1: not on what might have happened if things had gone
Speaker 1: a different way. We can't be sure. But more than that, though,
Speaker 1: we're still talking about twenty thousand people losing their lives
Speaker 1: and all the ripple effects that that makes throughout society,
Speaker 1: and moreover, we have machines that are at the fault
Speaker 1: for those twenty thousand lives being lost, and the idea
Speaker 1: that people have built machines that, through a failure of
Speaker 1: some sort or another, resulted in deaths is a very
Speaker 1: difficult proposition to accept. Also, it's just a key to
Speaker 1: think of in terms like that. I mean, clearly, one
Speaker 1: death is too many. We don't want to see anyone
Speaker 1: die in a car accident. Having a discussion in which
Speaker 1: you compare a fewer number of deaths and referring to
Speaker 1: it as quote unquote better is something that's pretty hard
Speaker 1: for us to process. It's easier to do it the
Speaker 1: other way, right, I mean, it's obvious that forty thousand
Speaker 1: people dying is worse than twenty thou people dying, But
Speaker 1: it's hard to view it the other way because anyone
Speaker 1: dying at all is awful. Now. Part of this also
Speaker 1: really boils down to a fear of handing over control
Speaker 1: to a machine. I know a lot of people bulk
Speaker 1: at that idea. They don't like the idea of not
Speaker 1: being the actual entity making decisions behind the wheel. Confronting
Speaker 1: them with statistics showing how human error leads to catastrophe,
Speaker 1: doesn't tend to sway them. I mean a lot of
Speaker 1: people think, well, yeah, that's other people. I am not
Speaker 1: that person. Also, to be fair, we don't have the
Speaker 1: evidence to show that computers would necessarily be better, so
Speaker 1: they're something to that right now. Okay, let's let's get
Speaker 1: back to where we were in our history. The Grand
Speaker 1: Challenges helped set the stage for the next phase of development,
Speaker 1: which was mostly the realm of startups and some big companies,
Speaker 1: namely Google, would hire participants from the Grand and Urban
Speaker 1: Challenges to come and work in new divisions dedicated to
Speaker 1: creating driverless cars. The early pioneering work was now shifting
Speaker 1: pun intended into a phase of rapid iteration, as engineers
Speaker 1: and computer scientists and mechanics began to refine technologies to
Speaker 1: help make them better. So going from the first sort
Speaker 1: of proof of concept approach to how do we make
Speaker 1: this a better design so it does the thing it
Speaker 1: does but more effectively. Google's program began in earnest around
Speaker 1: two thousand nine, not long after the Urban Challenge. In
Speaker 1: twenty ten, publications began to report on the project. So
Speaker 1: it's been secret for about a year, maybe almost two years.
Speaker 1: Google had been testing vehicles in and around the Mountain View, California,
Speaker 1: headquarters for the company. And while the vehicles still had
Speaker 1: manual controls and they still had a driver behind the wheel,
Speaker 1: there were at least some segments of some of these
Speaker 1: test drives that felt totally under the control of the
Speaker 1: vehicle itself. It was ranking up miles of autonomous driving experience.
Speaker 1: It was gathering data, and those people who are working
Speaker 1: in the division use that data to further refine their approach.
Speaker 1: By the company had logged more than one hundred forty
Speaker 1: thousand miles driven by autonomous vehicles, which equals out to
Speaker 1: around two five thousand kilometers. And that's pretty, you know,
Speaker 1: respectable distance. But let's compare that against the miles that
Speaker 1: were driven by human drivers in the United States. So
Speaker 1: in US drivers accumulated nearly three trillion miles trillion. So
Speaker 1: that means if you were to do a percentage and
Speaker 1: you were to say how many how much percentage of
Speaker 1: miles did Google cars drive compared to human drivers in
Speaker 1: the US, in the Google cars would account for about
Speaker 1: point zero zero zero zero zero four seven percent of
Speaker 1: all miles traveled vehicle miles. So not you you can
Speaker 1: call it a fraction of a percent, But even that
Speaker 1: is being generous. It's a fraction of a fraction of
Speaker 1: a fraction of a percent. Now, if you're familiar with
Speaker 1: the idea of things like conducting surveys, you know that
Speaker 1: sample size is really important. Right, If you ask five
Speaker 1: people a question, extrapolating those five answers to try and
Speaker 1: apply it to the population at large is a bad idea.
Speaker 1: It's not a good sample size. You don't have enough
Speaker 1: data to draw any conclusions. It's definitely bad science. So
Speaker 1: it makes little sense to compare the results of autonomous
Speaker 1: vehicles that haven't even come close to accumulating a percentage
Speaker 1: of the my was driven by the population at large.
Speaker 1: You cannot compare the two because the experience is so
Speaker 1: monumentally different. Now, for several years, Google's cars operated without
Speaker 1: any accidents, at least not any that were the fault
Speaker 1: of the driverless car itself. There were a few incidents,
Speaker 1: but they either happened when the safety driver was operating
Speaker 1: the car, so a human driver was driving the Google
Speaker 1: car not autonomous vehicle mode, or there there was the
Speaker 1: fault of some other driver. Right, someone in a totally
Speaker 1: different car got into an accident with a Google car,
Speaker 1: and it wasn't the fault of the autonomous system, but
Speaker 1: rather the other driver. Those were really the only two
Speaker 1: kind of categories of incidents that happened in the early
Speaker 1: days of Google's testing. So at first glance, it looked
Speaker 1: like the driverless cars were truly safer than a human
Speaker 1: operated vehicle. Right, They had a much better record than
Speaker 1: human drivers did, and it may very well be the
Speaker 1: case that they were in fact much much safer than
Speaker 1: human drivers. But we have to go back to the
Speaker 1: sense of scale here. So in the United States, drivers
Speaker 1: travel more than three trillion miles by vehicle per year.
Speaker 1: I think the most recent one was almost three point
Speaker 1: three trillion. We're getting ridiculously high in numbers, and there
Speaker 1: are around forty thousand fatalities per year. And for the
Speaker 1: sake of this example, will assume all of those fatalities
Speaker 1: were caused by human error or bad decisions, just to
Speaker 1: simplify things. So if we do some rough math, we'll
Speaker 1: see that that amounts to one death per seventy five
Speaker 1: million miles driven. Now that's my estimate just based on
Speaker 1: back of the napkin. The actual estimates even more generous
Speaker 1: than that. The National Safety Council estimates that there's one
Speaker 1: point to five deaths per one hundred million vehicle miles driven.
Speaker 1: So what does that mean for autonomous vehicles, Well, they
Speaker 1: haven't driven close to a hundred million vehicle miles. It
Speaker 1: means those early days when we first learned that Google
Speaker 1: had launched its project, there were so few miles accumulated
Speaker 1: that you can't draw any meaningful conclusions. Now. To be fair,
Speaker 1: I don't think many people were trying to argue that
Speaker 1: autonomous car technology as it was in two ten, was
Speaker 1: already clearly superior to human driving. This was still an
Speaker 1: early testing phase. This was a point where it wasn't
Speaker 1: about showing that the technology was already better than humans.
Speaker 1: It was rather showing, hey, we've created technology that will
Speaker 1: allow this card to navigate and maneuver through human environments
Speaker 1: without making it a problem. So it wasn't even that
Speaker 1: our our standard is higher than human capability. It's more like,
Speaker 1: can this machine operate at the same level as humans
Speaker 1: within certain parameters pretty restrictive parameters. Skip ahead a few years,
Speaker 1: several companies invested in driverless car technologies that included big
Speaker 1: car companies, you know Toyota and Chrysler and others GM
Speaker 1: they've all invested huge amounts of money in autonomous car
Speaker 1: research and development. UH. It also included startup companies independent
Speaker 1: startups that either we're working on components for autonomous cars
Speaker 1: like light our systems, or they were attempting to convert
Speaker 1: or build fully autonomous vehicles themselves. And then there were
Speaker 1: ride hailing companies, most notably Uber, that we're also investing
Speaker 1: billions of dollars in this technology with an eye on
Speaker 1: replacing the fleets of human operated vehicles that were the
Speaker 1: bread and butter of their company to uh turn them
Speaker 1: all over to robotaxis. So instead of having human drivers
Speaker 1: over at Uber, you know, Uber at the highest level
Speaker 1: wants to replace them with autonomous vehicles for reasons that
Speaker 1: are complex but mostly come down to money. So meanwhile,
Speaker 1: consumer vehicles were getting more and more sophisticated, and higher
Speaker 1: end vehicles started sporting some really nifty features that relate
Speaker 1: to autonomous cars are semi autonomous in themselves. Some of
Speaker 1: them are more modest, like lane assist or breaking assist
Speaker 1: safety features. Some are a little more spectacular, like the
Speaker 1: self parking capabilities that some cars have where they can
Speaker 1: park themselves and and pull out of parking spaces all
Speaker 1: by themselves, like that's pretty cool. They weren't intended to
Speaker 1: make consumer cars autonomous, but were rather positioned as sort
Speaker 1: of value added options for cars, like this is something
Speaker 1: nifty this car has, other cars don't have it. Don't
Speaker 1: you want to buy this car? And they give a
Speaker 1: hint of what might be in days to come. In
Speaker 1: Elon Musk started talking about an autopilot like feature for cars,
Speaker 1: and sure enough, the following year, Tesla unveiled a driver
Speaker 1: assist suite of features called autopilot. Now, personally, and I've
Speaker 1: talked about this before, I think naming it autopilot was
Speaker 1: the wrong move. I feel like the word auto pilot
Speaker 1: has a loaded meaning to it. It conveys a sense
Speaker 1: that the car will take care of everything for you,
Speaker 1: and that's not necessarily the case. In fact, that's not
Speaker 1: the case at all. The company tried to walk that
Speaker 1: back a bit, uh not by renaming it, which I
Speaker 1: think they needed to do, but they included messages, and
Speaker 1: this they also need to do, But they included messages
Speaker 1: that said drivers were not meant to remove their hands
Speaker 1: from the wheel or to take their attention away from
Speaker 1: the road, that these systems can assist, but they don't
Speaker 1: replace the need for a driver, and you have to
Speaker 1: agree to that before you can enable the autopilot feature.
Speaker 1: So the goal was saying, well, you have to acknowledge
Speaker 1: the fact that no, this is not meant for it
Speaker 1: to be an autonomous car, and not to go off
Speaker 1: on too much of a tangent. But I feel as
Speaker 1: though Elon Musk might be a little too aggressive with
Speaker 1: his projections about autonomous cars. And I don't mean to
Speaker 1: suggest that Elon Musk and Tesla are interchangeable. I do
Speaker 1: see that happening a lot in techt circles, where people
Speaker 1: will use one or the other interchangeably, and they are
Speaker 1: two different entities. But maybe Tesla the company's bravado stems
Speaker 1: from Elon Musk's own personality. But whatever the case, autopilot
Speaker 1: has proven to have its own limitations, and we saw
Speaker 1: that manifest in some rather high profile and tragic accidents.
Speaker 1: Beginning in ten, there have been several fatal accidents involving
Speaker 1: Tesla vehicles operating in autopilot mode. The first one took
Speaker 1: place on January twenty, two thousand sixteen, in China, and
Speaker 1: the most recent examples I know about took place on
Speaker 1: December two, thousand, nineteen, and there are actually two crashes
Speaker 1: with fatalities that day involving Tesla vehicles reportedly engaged in autopilot.
Speaker 1: I say reportedly, because I don't have access to all
Speaker 1: the data, I don't know if conclusively they've discovered that
Speaker 1: both of these vehicles were actually operating an autopilot mode.
Speaker 1: One of these happening California and the other happened in Indiana,
Speaker 1: both in the United States on December nineteen. Now, Tesla
Speaker 1: states that autopilot is meant as a driver assist feature
Speaker 1: and it's only semi autonomous. But at the same time,
Speaker 1: Elon Musk has said repeatedly that his goal was to
Speaker 1: get a fully autonomous vehicle on the road by the
Speaker 1: end of twenty nineteen, which now has been pushed back
Speaker 1: to sometime in the first quarter of so there are
Speaker 1: some conflicting messages coming out. Since a fully autonomous car
Speaker 1: and I'm talking about something that we would at least
Speaker 1: classify as level four, if not level five, is well
Speaker 1: beyond just a driver assist mode. And I should also
Speaker 1: add that Tesla drivers have a responsibility to use these
Speaker 1: features safely and as intended. If someone is taking their
Speaker 1: attention off the road, or they're sitting back from their
Speaker 1: steering wheel, or they're taking a nap, or they're watching
Speaker 1: Netflix or whatever, that's dangerously irresponsible behavior, and they are
Speaker 1: accountable for it. I don't want to give the implication
Speaker 1: to you guys that I think Tesla the company is
Speaker 1: fully to blame in this case. I actually think it's
Speaker 1: a shared responsibility, and that you've got some drivers who
Speaker 1: are eager to test out admittedly really cool and technologically
Speaker 1: advanced features, and you have a company that might message
Speaker 1: out these features in a way that isn't perhaps the
Speaker 1: most realistic or responsible method. It's a really bad combination, right.
Speaker 1: You've got people who are tech heads who are eager
Speaker 1: to play with the newest stuff. You've got a company
Speaker 1: that's Bill's reputation on creating super cool new stuff. It's
Speaker 1: only natural that you get when you combine those two,
Speaker 1: you can get some bad situations if they haven't been
Speaker 1: messaged properly. And I really feel that Tesla bungledness that
Speaker 1: the rollout needed to be done in such a way
Speaker 1: where there was never the implicate atian that this was
Speaker 1: an autonomous mode. Uh. Saying hey it's not autonomous after
Speaker 1: you've already called it autopilot and put the idea in
Speaker 1: the into people's heads is a little late in the game.
Speaker 1: So I think that that all parties here share accountability.
Speaker 1: It's not just Tesla the company's fault, and it's not
Speaker 1: entirely the driver's faults, although I think it's more their
Speaker 1: fault than the company's. Honestly, I mean, we're all adults, right,
Speaker 1: you should be if you're driving a car, and if
Speaker 1: you're an adult, you should be able to make the
Speaker 1: determination of hey, this is a bad idea. I should
Speaker 1: also add that Tesla is not the only company that
Speaker 1: has had autonomous or semi autonomous vehicles involved in fatal accidents.
Speaker 1: There was a case in Tempe, Arizona, involving Evolvo that
Speaker 1: had been converted into a semi autonomous vehicle that was
Speaker 1: being operated under Uber and that car hit a pedestrian
Speaker 1: while an autonomous mode, and the pedestrian died as a
Speaker 1: result of that accident. So Tesla is not the only
Speaker 1: company that has had tragedy befall it due to you know,
Speaker 1: failures in autonomous systems. Getting back to the scale argument
Speaker 1: for a second, when we're talking about autonomous systems allegedly
Speaker 1: at fault for accidents that lead to fewer than a
Speaker 1: dozen deaths, you could say, like, well, it's all tragic.
Speaker 1: You never want to see anyone die. One death is
Speaker 1: really too many, but still twelve less than twelve, that's
Speaker 1: that's so much fewer than you know, forty thousand. And
Speaker 1: you might be tempted to say these are tragic accidents,
Speaker 1: but if you look at how many are caused by humans,
Speaker 1: there's really no comparison. But once again, you have to
Speaker 1: remember that humans account for way more vehicle miles traveled
Speaker 1: by several orders of magnitude. So really the only way
Speaker 1: you could compare the two is if you had autonomous
Speaker 1: systems driving as many miles as humans are driving, and
Speaker 1: then you'd have to see if they still stacked up favorably,
Speaker 1: if those numbers were still matching up are still mismatched,
Speaker 1: like if if a ton of this car is still
Speaker 1: accounted for, you know, uh, significantly fewer accidents. But we
Speaker 1: can't say that because the autonomous cars are driving far
Speaker 1: fewer miles than humans are. So it is true that
Speaker 1: most accidents involving autonomous vehicles seemed to be the fault
Speaker 1: of human drivers. You know, it's not like most of
Speaker 1: the accidents we hear about were caused by the autonomous
Speaker 1: vehicles themselves. It tends to be that someone else, some
Speaker 1: other human, caused the accident. But the case of these fatalities,
Speaker 1: it does look like it was the autonomous system at fault,
Speaker 1: and that's truly truly concerning um. And also, you know,
Speaker 1: when when it's when it's a person who's at fault.
Speaker 1: We understand that people make mistakes, and we can feel,
Speaker 1: at least in some cases, we can feel some sympathy
Speaker 1: for a person where perhaps the situation was truly out
Speaker 1: of their control, that that situation was was partcularly extreme
Speaker 1: or unusual, and so we can feel so some sympathy
Speaker 1: for the person. But when it's a machine, then we've
Speaker 1: already surrendered control up to it, and that's where it
Speaker 1: gets particularly scary. You know, we have to trust in
Speaker 1: the machine, and when the machine betrays that trust by failing,
Speaker 1: that's a big problem. So what happens when there are
Speaker 1: no controls at all? The humans can access more on
Speaker 1: that in just a moment but first, let's take another
Speaker 1: quick break. One of the challenges autonomous car companies and
Speaker 1: engineers have faced is how do you balance between computer
Speaker 1: and manual control of a car? You know, how should
Speaker 1: control switch from one to the other. When should an
Speaker 1: automated system take over to avoid an accident like a
Speaker 1: collision prevention system, or when should a driver be able
Speaker 1: to override autonomous commands and bring the vehicle under manual control.
Speaker 1: Doing this is not as straightforward as you might think,
Speaker 1: and and doing it in a way that's safe and
Speaker 1: has a seamless transition of control is really hard. But
Speaker 1: what if there's no question about it at all? Because
Speaker 1: there are no controls to take See back in twenty
Speaker 1: four Google showed off a driverless car prototype that had
Speaker 1: no steering wheel, had no accelerator, no brake pedal, so
Speaker 1: there were no controls for a human to take over.
Speaker 1: The car would only operate autonomously because there were no
Speaker 1: other options. The prototype worked with a smartphone app and
Speaker 1: acted as sort of a ride hailing or robo taxi service.
Speaker 1: Users could summon a car using the app and they
Speaker 1: would indicate where they were wanted to go within a
Speaker 1: very restricted range of operation. Like it was geo fenced,
Speaker 1: so you couldn't go beyond a certain border that was
Speaker 1: pretty limited, and that meant that the vehicle had a
Speaker 1: lot of variables reduced, right it It cut back on
Speaker 1: the types of conditions and routes and situations the car
Speaker 1: might encounter, and thus made the problems of having an
Speaker 1: autonomous car slightly less complicated. There's still opportunities for complications,
Speaker 1: but you've drastically reduced them because you've reduced the variables. Well.
Speaker 1: The vehicle used an electric motor that was good for
Speaker 1: about one miles of driving per charge, and it boasted
Speaker 1: a top speed of twenty five miles per hour. So
Speaker 1: this little car would only really be suitable for transportation
Speaker 1: and restricted situations such as the campus of a big
Speaker 1: company like I don't know, Google. It wasn't intended as
Speaker 1: a practical vehicle for widespread adoption, but rather another iterative
Speaker 1: step towards fully autonomous cars. The robotaxi vision is one
Speaker 1: that tends to be the most common across the autonomous
Speaker 1: car space. That's largely because the technology used to of
Speaker 1: cars autonomy, you know, the the sensors, computers, robotic systems,
Speaker 1: that kind of stuff they don't come cheap, and a
Speaker 1: vehicle would cost significantly more than a manually operated vehicle
Speaker 1: a traditional car, So most experts agree that the future
Speaker 1: of autonomous cars, at least in the near term, will
Speaker 1: be in fleets that are operated by companies like Uber
Speaker 1: or Lift. They will be ride healing vehicles or robo taxis,
Speaker 1: and they will take passengers to their destinations, and then
Speaker 1: those cars will then move on to pick up their
Speaker 1: next fair, or they'll return to some sort of h
Speaker 1: Q for recharging or maintenance or whatever. It's unlikely that
Speaker 1: we're gonna see autonomous vehicles offered up for private ownership
Speaker 1: right away for the most part, due to the prohibitive
Speaker 1: expense of this additional technology. The Google's experiment pointed out
Speaker 1: both the advances of the tech and the limitations of
Speaker 1: autonomous car technology. Yeah, the car had no controls, which
Speaker 1: is what you would expect only if you had a
Speaker 1: level five autonomous car. But it also had very strict
Speaker 1: geo fencing restrictions and operational restrictions, so it couldn't go
Speaker 1: very fast, it couldn't venture very far, it wouldn't likely
Speaker 1: encounter unusual situations. So because of that, it wouldn't be
Speaker 1: Level five anyway, because you've you've limited the scenarios where
Speaker 1: it would be operating in the first place, it would
Speaker 1: not be driving into all the different situations that a
Speaker 1: human driver would encounter. A truly autonomous vehicle would need
Speaker 1: to be able to handle everything, all sorts of unpredictable situations.
Speaker 1: The average person isn't likely to encounter a truly unusual
Speaker 1: experience on any given drive, right, It's not like if
Speaker 1: you drive down the road you're going to see every
Speaker 1: single outlier. That's very unlikely. However, when you have a
Speaker 1: collective three trillion vehicle miles traveled per year, you're bound
Speaker 1: to get some pretty extreme situations somewhere in those three
Speaker 1: trillion miles. So you might have a person who has
Speaker 1: to drive through a dangerous environment, like maybe mud slides
Speaker 1: are coming across a road, or when people were evacuating
Speaker 1: parts of California that were affected by wildfires, or there
Speaker 1: might be you know, animals in the road. There could
Speaker 1: be people in the road. Weather effects can be unpredictable,
Speaker 1: and they can change driving conditions rapidly. There are all
Speaker 1: sorts of things that humans encounter every year, with varying
Speaker 1: degrees of success and maneuvering around or through them. And
Speaker 1: if we actually do see autonomous cars take up more
Speaker 1: of the car landscape, those autonomous cars are also going
Speaker 1: to encounter those situations too. It's just a matter of
Speaker 1: the odds, you know. And there are a lot of
Speaker 1: unanswered questions about how these cars are going to deal
Speaker 1: with those situations when they arise, and that includes the
Speaker 1: famous trolley problem dilemma. Now, in the classic trolley problem,
Speaker 1: you're presented with a hypothetical situation in which a trolley
Speaker 1: is out of control. It's moving down the tracks, uh
Speaker 1: and it cannot stop. So if you do nothing, if
Speaker 1: you do not act, the trolley will continue down the
Speaker 1: track and it's going to hit a group of five people.
Speaker 1: It's gonna there's no doubt it will kill those five people. However,
Speaker 1: there next to a lever, and if you pull that lever,
Speaker 1: you will send the trolley down a side track, so
Speaker 1: it will miss the five people, but it will definitely
Speaker 1: hit and kill one person. So if you do nothing,
Speaker 1: five people die, But if you act, one person dies.
Speaker 1: So does making the choice to pull the lever amount
Speaker 1: to murdering that one person? Did you just choose to
Speaker 1: kill that person. Does doing nothing mean that you've murdered
Speaker 1: five people or does it just mean that you allowed
Speaker 1: five people to die? Is there any meaningful difference between
Speaker 1: those two things. Well, these are all questions and ethics,
Speaker 1: but with autonomous cars it gets into less hypothetical territory.
Speaker 1: You have to actually start to answer these questions. Cars
Speaker 1: may very well encounter, since you, ations in which there
Speaker 1: is no way to avoid injuring or killing someone. So
Speaker 1: in those cases, what do the cars do you know who?
Speaker 1: How do the cars choose which person is to be
Speaker 1: put at risk? How do they decide what action to take?
Speaker 1: Do they try to protect the people who are inside
Speaker 1: the car at all costs, so in other words, yeah,
Speaker 1: we're gonna make this decision which will protect the people
Speaker 1: who are inside the car. By anyone else there they
Speaker 1: are fair game. Or do they try to protect people
Speaker 1: who are outside the car who maybe don't have the
Speaker 1: benefit of the car's other safety features. Maybe you build
Speaker 1: it into an autonomous car that the people inside the
Speaker 1: car are allowed to encounter a bit more risk because
Speaker 1: your thought is, well, the inside the car is very safe,
Speaker 1: so we want to make sure we protect say a
Speaker 1: pedestrian or bicyclist. We don't want the car to hit
Speaker 1: them because they will suffer way more damage than the
Speaker 1: people inside would. So we're going to make that decision.
Speaker 1: That's a that's a possible choice too, But these are
Speaker 1: not necessarily answered questions. There are questions that are being
Speaker 1: answered as people are designing these vehicles. One benefit that
Speaker 1: autonomous cars might have is that organizations overseeing them could,
Speaker 1: at least in theory, use the collective information across an
Speaker 1: entire fleet of autonomous cars to improve performance of each
Speaker 1: vehicle within that fleet. So if one car were to
Speaker 1: encounter a really unusual experience, engineers could take the data
Speaker 1: from that experience and tweak the behavior of all the
Speaker 1: cars across the fleet. So when one individual encounters something new,
Speaker 1: everyone learns from that experience. So it's sort of like
Speaker 1: the borg in Star Trek. It's a collective and that's
Speaker 1: a big advantage over human beings, right because when it
Speaker 1: comes to humans, the person who experienced something, they might
Speaker 1: learn from that experience, but that that learning, that knowledge
Speaker 1: doesn't automatically spread across the population and general So in
Speaker 1: that way, autonomous cars can have a big advantage over
Speaker 1: human drivers. If that is used properly on the flip side,
Speaker 1: when it comes to something as potentially deadly as a vehicle,
Speaker 1: it's pretty cavalier to say, well, the cars will learn
Speaker 1: as they go, and we'll apply that knowledge to all
Speaker 1: the vehicles. They'll get better the longer they drive, because
Speaker 1: if learning also includes accidents that could potentially result in
Speaker 1: injuries or fatalities, that's a really steep price to pay
Speaker 1: for knowledge. And we're seeing more companies developed vehicles that
Speaker 1: have no manual control systems. You know, Google came out
Speaker 1: with There's in but that's not the only case of it.
Speaker 1: In January, g MS Autonomous Car division, which is called Cruise.
Speaker 1: Originally it was an independent startup, but GM gobbled them up.
Speaker 1: A couple of years ago, they unveiled a driverless car
Speaker 1: called Origin. And the Origin, like Google's prototype, has no
Speaker 1: steering wheel, has no accelerator, no brake pedal. It has
Speaker 1: seats that all face inward. They're kind of like, you know,
Speaker 1: think imagine two benches with with backs, but the two
Speaker 1: benches are facing each other, so the people sitting in
Speaker 1: what would consider to be the front of the vehicle
Speaker 1: would have their backs to the windshield and they'd be
Speaker 1: looking back at the people sitting in the back seats,
Speaker 1: who'll be looking forward. Uh. Now, it's about the size
Speaker 1: of a crossover suv, and that means there's a pretty
Speaker 1: good amount of space inside the vehicle. So while you
Speaker 1: are facing the other folks, like if if you're in
Speaker 1: the front seat, you're facing the folks in the back
Speaker 1: and they're facing you. Because there's so much space, you're
Speaker 1: not likely to accidentally kick each other or anything. It
Speaker 1: looks pretty roomy. On top of that, the car has
Speaker 1: a cool little keypad on the doors. And the idea
Speaker 1: is that a production model of this car would be
Speaker 1: used like a robotaxi. So you would hail a ride
Speaker 1: on your smartphone and this little robo car would come
Speaker 1: driving up to you, and then it would give you
Speaker 1: a multi digit pass code. You would get one on
Speaker 1: your app and you would look at that ask God,
Speaker 1: and you would type the numbers into the keypad and
Speaker 1: that would open the doors. So that way, you know,
Speaker 1: some unauthorized person wouldn't just jump into your car and
Speaker 1: then go gallivanting off without you. You would be able
Speaker 1: to unlock the car yourself because you had a one
Speaker 1: time use key code. That's a decent concept for a
Speaker 1: working robotaxi, but the fact remains that we haven't hit
Speaker 1: level five autonomy yet. At best, we have limited level four.
Speaker 1: Most of the vehicles we've seen in testing can perform autonomously,
Speaker 1: but only with pretty tight restrictions like that along specific
Speaker 1: predefined routes or within very strict geo fencing, or at
Speaker 1: particular times of the year or even particular times of
Speaker 1: the day. Again, that helps reduce the variables that the
Speaker 1: car mine encounter on any given day, and it gives
Speaker 1: it the best chance to operate safely, but that really
Speaker 1: limits how useful the cars are in practical applications. For
Speaker 1: autonomous cars to work as an alternative to manually controlled vehicles,
Speaker 1: they need to brand and pretty much all the same
Speaker 1: conditions that regular cars do without restrictions, and we just
Speaker 1: aren't there yet, and we might not be for several
Speaker 1: more decades. The Prognos Research Institute actually identified four factors
Speaker 1: that are in the way of autonomous vehicles. They include
Speaker 1: technological maturity, which is what I was just talking about.
Speaker 1: Infrastructure development, so having you know, cities that are designed
Speaker 1: in such a way that they can allow for autonomous
Speaker 1: cars the inertia of the fleet. This means that you know,
Speaker 1: there's a ton of manually controlled vehicles out in the
Speaker 1: world already, right The vast majority of cars that are
Speaker 1: out there are manual control vehicles. They might have some
Speaker 1: limited autonomy, but for the most part, they're controlled by humans.
Speaker 1: It would take a very long time before autonomous vehicles
Speaker 1: represent a significant percentage of the overall vehicles on the road,
Speaker 1: let alone a majority. So it will take many, many,
Speaker 1: many years to wean off of a human controlled cars
Speaker 1: and go to autonomous cars, barring any legislation that outlaws
Speaker 1: vehicles um or human controlled vehicles, I guess I should say.
Speaker 1: And then finally we have legal hurdles to overcome the
Speaker 1: regulations that are going to be coming out around driverless cars.
Speaker 1: We're seeing a lot of money poured into research and
Speaker 1: development to push the technological limits further and to establish
Speaker 1: the foundation for truly autonomous vehicles. But I wonder if
Speaker 1: these various companies and their investors are really in it
Speaker 1: for the long haul, so to speak, because I suspect
Speaker 1: it's going to take a pretty long time to get
Speaker 1: to a point where we feel there's really reliable safe
Speaker 1: level five autonomous vehicles in the world, let alone a
Speaker 1: world in which governments have also agreed and have caught
Speaker 1: up and have defined the legal parameters for the operation
Speaker 1: of these vehicles. Because you know, it's one thing to
Speaker 1: prove the technology works. That doesn't necessarily mean that technology
Speaker 1: will be legal to to operate, right Like, governments tend
Speaker 1: to move a lot more slowly than technology does. So
Speaker 1: if investors are willing to play the long game, then
Speaker 1: I think their investments will ultimately pay off. But it's
Speaker 1: going to take a long time, which means lots of
Speaker 1: repeated investments are going to be required to keep these
Speaker 1: companies going, to keep them innovating and improving technologies. And meanwhile,
Speaker 1: there's not going to be an actual market for them
Speaker 1: to capitalize on outside a few, you know, test programs
Speaker 1: that don't really count, because there's no way that the
Speaker 1: revenue they're generating is actually eclipsing the cost of operation.
Speaker 1: It's got to be a money losing proposition right now
Speaker 1: in all the different test cases at scale, with fully
Speaker 1: legal vehicles that are embraced by the general public shore
Speaker 1: it could work from a financial standpoint right now, though
Speaker 1: it's all just proof of concept that hasn't uh seem
Speaker 1: full fruition. Now. I still believe in autonomous cars. I
Speaker 1: still believe they will ultimately make the roads safer and
Speaker 1: reduce the number of deaths and injuries from car accidents.
Speaker 1: I just think it's going to take a lot longer
Speaker 1: that I had previously imagined. And that's not necessarily a
Speaker 1: bad thing. This isn't important enough issue that we have
Speaker 1: to make sure we get it right that we can
Speaker 1: deploy vehicles in ways that makes sense, that are truly safe,
Speaker 1: that are ethical, that there are in as an ideal
Speaker 1: implementation as we can manage UH. And we have to
Speaker 1: make sure that it makes financial sense too, right. We
Speaker 1: need to have h make sure that it truly represents
Speaker 1: an affordable way to get around that eliminates the need
Speaker 1: for stuff like garages and parking lots and dense urban centers.
Speaker 1: Those areas could be reclaimed and used for other stuff,
Speaker 1: and that stuff might be far more productive than just
Speaker 1: being a storage place for a car when it's not
Speaker 1: being in use. Personal ownership could really be on a
Speaker 1: serious decline in that kind of future, replaced with on
Speaker 1: demand car service, and the cars that are in service
Speaker 1: would be used much more frequently rather than just sitting
Speaker 1: idle and taking up space for the vast majority of
Speaker 1: their existence. If you think about your average car um
Speaker 1: it's the amount of time you're actually using it versus
Speaker 1: the amount of time it's just sitting there doing nothing
Speaker 1: is staggering, right. So if you're able to make more
Speaker 1: use of the vehicle, uh, then it's a more efficient
Speaker 1: use of the technology. It's a it's a better investment
Speaker 1: for all the the materials that went into making that vehicle.
Speaker 1: So you could argue, well, this makes more sense from
Speaker 1: multiple perspectives if we're able to make better use of
Speaker 1: this technology and not just have it sitting someplace taking
Speaker 1: up room. But it's it's a lot a lot of
Speaker 1: things have to fall into place for that future to
Speaker 1: come true. I think it's a future that it makes sense,
Speaker 1: but only if we can get the tech just right
Speaker 1: and before then, what we're really risking is making bad
Speaker 1: decisions that just make it harder to get to the
Speaker 1: right future. So we have to be careful in how
Speaker 1: we're testing these things. We have to minimize risk while
Speaker 1: maximizing our our ability to learn things, which is a
Speaker 1: very tricky thing to do because Ultimately, you do have
Speaker 1: to start deploying autonomous cars into populated centers or else.
Speaker 1: All you've done is created something that works really well
Speaker 1: in the lab, but not well in the real world,
Speaker 1: and that would be useless to us because most of
Speaker 1: us don't live in a lab. I know I don't,
Speaker 1: not since two thousand fourteen, but that's another story. Guys.
Speaker 1: If you have any suggestions for future topics for tech Stuff,
Speaker 1: reach out to me. You can find me on social media.
Speaker 1: I'm on Facebook and on Twitter with the handle tech
Speaker 1: stuff h s W and I'll talk to you again
Speaker 1: really soon. Hext Stuff is a production of I Heart
Speaker 1: Radio's How Stuff Works. For more podcasts from I heart Radio,
Speaker 1: visit the I heart Radio app, Apple Podcasts, or wherever
Speaker 1: you listen to your favorite shows.