1
00:00:00,080 --> 00:00:06,960
i don't think anything says we're living in the future quite like the

2
00:00:04,000 --> 00:00:11,840
proliferation of self-driving cars now granted they're not exactly all over the

3
00:00:09,040 --> 00:00:15,759
roads yet but given the huge investments that automakers and tech companies are

4
00:00:13,920 --> 00:00:22,800
making in them it seems like it's a matter of when rather than if

5
00:00:19,439 --> 00:00:25,039
but how exactly do they work i mean most

6
00:00:22,800 --> 00:00:28,720
of our trains aren't even self-driving and those things are quite literally on

7
00:00:27,519 --> 00:00:33,680
rails well as you'd expect self-driving cars

8
00:00:31,359 --> 00:00:38,320
are jam-packed with tons of equipment to help them both see and understand their

9
00:00:36,480 --> 00:00:43,280
environment some of it is rather familiar such as a data connection for

10
00:00:40,559 --> 00:00:48,399
traffic info and a gps transponder to allow the car to know where it is but

11
00:00:46,000 --> 00:00:53,120
while gps is fine for providing driving directions in a human-controlled car it

12
00:00:50,960 --> 00:00:58,719
has a margin of error of up to several meters and besides to control a car with

13
00:00:56,000 --> 00:01:04,320
such a slow and error prone system would result in more than a few fender benders

14
00:01:01,359 --> 00:01:08,799
so to start with self-driving cars also use freaking laser beams to build a map

15
00:01:07,200 --> 00:01:13,119
of what's around them lidar which you can learn more about up

16
00:01:10,400 --> 00:01:17,840
here can be built into units that can spin

17
00:01:14,280 --> 00:01:20,320
360 degrees shooting invisible lasers in

18
00:01:17,840 --> 00:01:25,360
all directions then measuring how long it takes for each beam to hit an object

19
00:01:23,040 --> 00:01:30,080
and bounce back this allows the car to judge not only the distances but even

20
00:01:27,759 --> 00:01:34,479
the shapes of the objects immediately around it giving the car even more

21
00:01:32,159 --> 00:01:38,479
information is traditional radar to help it gauge speed gyroscopes and

22
00:01:36,400 --> 00:01:42,320
accelerometers to provide more movement data than would be possible with a

23
00:01:39,920 --> 00:01:46,240
traditional speedometer and high resolution cameras now you might think

24
00:01:44,960 --> 00:01:50,720
well we've already got frickin laser beams

25
00:01:48,479 --> 00:01:55,040
what are the cameras for well although the car's other systems give it a pretty

26
00:01:53,119 --> 00:02:00,479
good idea of what's in the general vicinity the cameras really help provide

27
00:01:57,920 --> 00:02:04,479
a complete picture as they can see in color this helps your car distinguish

28
00:02:02,479 --> 00:02:10,080
let's say a caution sign from a construction sign so you put all of this

29
00:02:06,960 --> 00:02:12,480
together and the car has a coherent 3d

30
00:02:10,080 --> 00:02:17,680
map that provides the data that it needs to make decisions for example lidar and

31
00:02:15,760 --> 00:02:20,879
video can determine whether that thing up ahead is telling the car to stop or

32
00:02:20,000 --> 00:02:27,520
yield whether the vehicle in front is a small sports car or a large truck so that it

33
00:02:24,879 --> 00:02:32,400
can decide whether to pass or whether that two-wheeled contraption is a

34
00:02:29,520 --> 00:02:35,440
motorcycle or a bicycle so the car can get around the cyclist and give her a

35
00:02:34,000 --> 00:02:39,599
bit more space also many self-driving cars have

36
00:02:37,440 --> 00:02:43,680
ultrasonic sensors in the wheels so the car will know how close it is to the

37
00:02:41,280 --> 00:02:49,200
curb and other vehicles while parking and even microphones so that they can

38
00:02:46,319 --> 00:02:53,200
hear a police or ems siren to get out of the way in a timely manner i mean that's

39
00:02:51,760 --> 00:02:59,920
going to be one improvement on the roads around here once self-driving cars are ubiquitous people can be so rude they're

40
00:02:57,360 --> 00:03:04,239
going to an emergency anyway that's how all the data gets collected

41
00:03:02,319 --> 00:03:08,319
but what about the processing an experienced driver can take in

42
00:03:06,239 --> 00:03:12,000
information from the environment filter out everything that's not important and

43
00:03:10,319 --> 00:03:15,120
make all the kinds of decisions we talked about before in a fraction of a

44
00:03:14,159 --> 00:03:19,760
second so of course then you need a lot of

45
00:03:17,440 --> 00:03:25,040
computing power for a self-driving car to pull off the same thing this is made

46
00:03:22,000 --> 00:03:27,680
possible by running many processors in

47
00:03:25,040 --> 00:03:31,519
parallel to crunch these numbers it's actually quite similar to how desktop

48
00:03:30,000 --> 00:03:36,159
gpus work these types of processors also happen to

49
00:03:33,760 --> 00:03:41,519
be more trainable with machine learning so for example you can teach a car what

50
00:03:38,799 --> 00:03:46,080
a pedestrian looks like by showing it a large data set with lots of photos of

51
00:03:43,599 --> 00:03:50,799
people crossing the road but what if a self-driving car didn't only have to

52
00:03:48,000 --> 00:03:54,879
rely on its own sensors and processors well another goal is to have these cars

53
00:03:52,720 --> 00:03:59,599
communicate with each other while they're on the road so they can

54
00:03:56,879 --> 00:04:04,720
actually tell other cars what they're doing and why enhancing safety and

55
00:04:02,959 --> 00:04:09,200
taking some of the guesswork away from the individual vehicles and if you think

56
00:04:07,040 --> 00:04:13,120
about it having many cars working together like this in unison could help

57
00:04:11,200 --> 00:04:17,040
with lots of other issues too like easing congestion by coordinating

58
00:04:15,040 --> 00:04:20,560
movement so that traffic can keep flowing smoothly

59
00:04:18,639 --> 00:04:25,680
also in the future especially once faster 5g connections become more common

60
00:04:23,280 --> 00:04:30,000
we could even see smart infrastructure that communicates with the cars i mean

61
00:04:28,080 --> 00:04:33,919
think about a parking garage that could tell a car that it's too full to

62
00:04:31,520 --> 00:04:37,759
accommodate it or transponders in construction zones that could tell them

63
00:04:35,520 --> 00:04:41,600
to slow down and prepare for narrower lanes of course we do still have a long

64
00:04:40,000 --> 00:04:45,759
way to go before this kind of tech is commonplace but given how many

65
00:04:43,919 --> 00:04:50,479
semi-autonomous cars are available for purchase today and how much money is

66
00:04:48,720 --> 00:04:54,880
being poured into this whole endeavor along with the ever-rising processing

67
00:04:52,560 --> 00:04:58,160
and data speeds we continue to enjoy the hope

68
00:04:55,759 --> 00:05:03,199
is that in the near future our car trips will be much easier and safer since well

69
00:05:01,040 --> 00:05:07,680
over 90 percent of car accidents are attributable to human error and speaking

70
00:05:05,440 --> 00:05:12,720
of self-driving cars today's episode was brought to you by ibm spectrum storage

71
00:05:10,400 --> 00:05:19,039
did you know that a self-driving car can generate up to 15 terabytes of data

72
00:05:16,080 --> 00:05:24,400
every hour and lots of this is actually not even used while they're driving so

73
00:05:22,000 --> 00:05:29,840
it's really common in the ai industry to have to ingest just tons of this raw

74
00:05:26,960 --> 00:05:35,039
data and transform it after the fact while the car's not even driving into

75
00:05:32,240 --> 00:05:38,560
intelligence and ibm is using spectrum storage to help automakers manage all

76
00:05:37,520 --> 00:05:42,400
this data so it's optimized for ai and machine

77
00:05:40,639 --> 00:05:47,360
learning with industry-leading GPU accelerated servers and ibm spectrum

78
00:05:44,880 --> 00:05:51,440
scale software-defined storage combined with groundbreaking performance and

79
00:05:49,120 --> 00:05:55,039
simplified deployment so get the fastlane to insights into

80
00:05:53,039 --> 00:05:59,919
whatever field you're in by learning more about data storage for ai and ibm

81
00:05:57,919 --> 00:06:04,000
storage solutions for autonomous driving at the link below so thanks for watching

82
00:06:02,160 --> 00:06:07,680
guys like dislike check out our other videos leave a comment with video

83
00:06:05,520 --> 00:06:11,919
suggestions and don't forget to subscribe and follow

84
00:06:10,000 --> 00:06:16,160
not this one i mean this one too but but but also Techquickie that's a button

85
00:06:13,600 --> 00:06:16,160
below