1 00:00:04,000 --> 00:00:07,360 Speaker 1: When you hear the words driverless vehicle, you might picture 2 00:00:07,400 --> 00:00:10,600 Speaker 1: one of those proto types in YouTube videos. You know, 3 00:00:10,680 --> 00:00:13,640 Speaker 1: a small hatchback with a big gizmo on the roof 4 00:00:13,680 --> 00:00:17,400 Speaker 1: and sensors sticking out all over the place and nobody 5 00:00:17,480 --> 00:00:21,080 Speaker 1: in the driver's seat. Or maybe you think of Tesla's 6 00:00:21,200 --> 00:00:24,919 Speaker 1: getting closer but not quite there yet autopilot feature. 7 00:00:25,280 --> 00:00:29,440 Speaker 2: According to the Tesla website, autopilot, enhanced autopilot, and full 8 00:00:29,480 --> 00:00:33,479 Speaker 2: self driving features do not make their vehicles autonomous. Drivers 9 00:00:33,560 --> 00:00:36,159 Speaker 2: must be fully attentive with their hands on the wheel. 10 00:00:36,960 --> 00:00:40,640 Speaker 1: The promise of truly autonomous vehicles has attracted a lot 11 00:00:40,640 --> 00:00:44,600 Speaker 1: of attention over the years and a lot of investor money. 12 00:00:44,840 --> 00:00:48,000 Speaker 3: Meanwhile, Fiat has announced that it has joined a bmwled 13 00:00:48,040 --> 00:00:50,880 Speaker 3: consortium to develop self driving car technology. 14 00:00:51,159 --> 00:00:54,480 Speaker 4: Madrapan SoftBank has announced they will invest two and a 15 00:00:54,600 --> 00:00:57,080 Speaker 4: quarter billion dollars in General Motors. 16 00:00:57,120 --> 00:01:01,480 Speaker 1: Self driving car unit called the Crews, and self driving 17 00:01:01,520 --> 00:01:03,960 Speaker 1: cars are starting to show up on the roads in 18 00:01:04,040 --> 00:01:06,240 Speaker 1: states like California and Arizona. 19 00:01:06,880 --> 00:01:10,640 Speaker 5: Wayima, formerly known as Google's self driving car project, will 20 00:01:10,680 --> 00:01:14,640 Speaker 5: soon start offering rides to the public in self driving vans. 21 00:01:15,160 --> 00:01:19,840 Speaker 1: There's also still plenty of skepticism though, especially whenever there's 22 00:01:19,880 --> 00:01:22,680 Speaker 1: an accident involving one of these test vehicles. 23 00:01:23,360 --> 00:01:25,400 Speaker 4: Well, this morning, a woman is in the hospital after 24 00:01:25,400 --> 00:01:28,520 Speaker 4: sufferank critical injuries after she was hit by a driverless 25 00:01:28,520 --> 00:01:31,000 Speaker 4: car in downtown San Francisco last night. 26 00:01:31,080 --> 00:01:34,240 Speaker 1: Of course, but with so much attention on cars, you 27 00:01:34,360 --> 00:01:37,120 Speaker 1: might not know that one of the fastest growing areas 28 00:01:37,120 --> 00:01:41,080 Speaker 1: in the push toward driverless vehicles is actually trucks, and 29 00:01:41,160 --> 00:01:45,160 Speaker 1: not pickups, but box trucks and the massive eighteen wheelers 30 00:01:45,200 --> 00:01:48,080 Speaker 1: on the highway that deliver goods all over the US. 31 00:01:48,760 --> 00:01:53,360 Speaker 1: Several US companies, including Kodiak, Gaddick and Aurora, are now 32 00:01:53,400 --> 00:01:57,760 Speaker 1: testing autonomous trucks on the open road in Texas. Bloomberg 33 00:01:57,800 --> 00:02:01,520 Speaker 1: Transportation reporter Thomas Black has taking a ride in these trucks, 34 00:02:01,600 --> 00:02:04,040 Speaker 1: and he's here to talk about what these companies are 35 00:02:04,040 --> 00:02:06,720 Speaker 1: trying to do and the high bar they have to 36 00:02:06,760 --> 00:02:07,640 Speaker 1: meet to succeed. 37 00:02:08,360 --> 00:02:12,160 Speaker 3: All these companies know that they have to be almost flawless, 38 00:02:12,320 --> 00:02:15,640 Speaker 3: if not perfect, on the technology as far as safety goes. 39 00:02:16,400 --> 00:02:19,960 Speaker 1: And later we hear from Chris Ermson, the CEO of Aurora, 40 00:02:20,280 --> 00:02:23,200 Speaker 1: about why he believes his eighteen wheelers will be on 41 00:02:23,360 --> 00:02:26,640 Speaker 1: the road without a driver as early as next year. 42 00:02:33,400 --> 00:02:36,840 Speaker 1: I'm Westksova today on the Big Take. Are you ready 43 00:02:36,880 --> 00:02:47,799 Speaker 1: to share the road with driverless trucks? Tom? I think 44 00:02:47,800 --> 00:02:50,840 Speaker 1: we're all aware that driverless cars have been in production 45 00:02:51,040 --> 00:02:52,920 Speaker 1: in various ways for a long time. But what I 46 00:02:52,960 --> 00:02:55,800 Speaker 1: did not know is that there's not one, but three 47 00:02:56,120 --> 00:02:59,440 Speaker 1: driverless truck companies right now. 48 00:03:00,080 --> 00:03:02,919 Speaker 5: Access there are, and there's going to be more. 49 00:03:03,240 --> 00:03:07,360 Speaker 3: It's a technology that probably has been proven out and 50 00:03:07,440 --> 00:03:10,280 Speaker 3: now it's proving its safety case, and then they're going 51 00:03:10,360 --> 00:03:12,200 Speaker 3: to roll out that safety case to people, and then 52 00:03:12,200 --> 00:03:14,120 Speaker 3: they're going to take drivers out and we're going to 53 00:03:14,160 --> 00:03:17,600 Speaker 3: see these on the roads if their pathway turns out 54 00:03:17,600 --> 00:03:19,000 Speaker 3: the way they think it's going to turn out. 55 00:03:19,760 --> 00:03:23,120 Speaker 1: Tell us about the companies that are making these trucks 56 00:03:23,120 --> 00:03:25,440 Speaker 1: and putting them on the road and exactly how they 57 00:03:25,440 --> 00:03:26,440 Speaker 1: were what they're doing. 58 00:03:27,320 --> 00:03:30,040 Speaker 3: So you have to think about these companies as software companies, 59 00:03:30,200 --> 00:03:32,200 Speaker 3: because they're not going to make the trucks. The trucks 60 00:03:32,200 --> 00:03:34,280 Speaker 3: are going to be made by the manufacturers that have 61 00:03:34,320 --> 00:03:37,600 Speaker 3: made trucks for decades and more. All these other truck 62 00:03:37,600 --> 00:03:40,400 Speaker 3: makers are going to provide the vehicles. And these guys 63 00:03:40,400 --> 00:03:43,360 Speaker 3: really are going to add obviously the software, and these 64 00:03:43,400 --> 00:03:45,040 Speaker 3: are big on board computers. 65 00:03:45,080 --> 00:03:47,080 Speaker 5: That's very important because you want. 66 00:03:46,880 --> 00:03:50,040 Speaker 3: To be able to have the vehicles self sustain it 67 00:03:50,080 --> 00:03:52,920 Speaker 3: doesn't need the Internet to be able to operate. And 68 00:03:52,960 --> 00:03:56,600 Speaker 3: then the mechanical side is really about redundancy. You want 69 00:03:56,640 --> 00:03:59,160 Speaker 3: to make sure if something failed, like the steering wheel, 70 00:03:59,320 --> 00:04:01,800 Speaker 3: that the thing doesn't careen off and hurt some people. 71 00:04:02,240 --> 00:04:04,760 Speaker 5: So they're going to have redundant. 72 00:04:04,360 --> 00:04:07,960 Speaker 3: Actuators and motors on the three things that we use 73 00:04:08,040 --> 00:04:10,840 Speaker 3: to drive a vehicle, the steering wheel, the brakes, and 74 00:04:10,920 --> 00:04:11,400 Speaker 3: the gas. 75 00:04:13,520 --> 00:04:15,840 Speaker 1: So tom of these are three different companies that are 76 00:04:15,840 --> 00:04:18,200 Speaker 1: doing it, and I imagine they each have a slightly 77 00:04:18,240 --> 00:04:22,080 Speaker 1: different approach to figuring out how you put a truck 78 00:04:22,400 --> 00:04:24,760 Speaker 1: on the highway without anyone driving it. 79 00:04:25,520 --> 00:04:28,400 Speaker 3: The approach behind how this is going to work with 80 00:04:28,440 --> 00:04:31,680 Speaker 3: the software and the mechanics is somewhat similar. It's about 81 00:04:31,960 --> 00:04:36,760 Speaker 3: having three basic types of sensors. You have cameras of course, 82 00:04:37,000 --> 00:04:40,200 Speaker 3: and then light r and then you have radar. So 83 00:04:40,279 --> 00:04:42,440 Speaker 3: you have the sensor package, and then the sensor package 84 00:04:42,440 --> 00:04:45,320 Speaker 3: connects to the computer and the computer has to analyze 85 00:04:45,360 --> 00:04:48,719 Speaker 3: all this data coming in and make the decisions and 86 00:04:48,760 --> 00:04:53,120 Speaker 3: then those decisions are translated into the actuators, which are 87 00:04:53,279 --> 00:04:56,159 Speaker 3: little things that can either turn or twist or whatever, 88 00:04:56,720 --> 00:05:00,920 Speaker 3: and then it'll be attached to again those three of driving, 89 00:05:01,000 --> 00:05:03,839 Speaker 3: the steering wheel, the gas, and the brake. That all 90 00:05:03,960 --> 00:05:07,360 Speaker 3: is pretty much the same. Really, where they might differ 91 00:05:07,600 --> 00:05:08,960 Speaker 3: is what part of the market. 92 00:05:08,800 --> 00:05:09,600 Speaker 5: They want to go after. 93 00:05:10,000 --> 00:05:12,960 Speaker 3: Got Tea, for example, wants to go after what they 94 00:05:12,960 --> 00:05:16,440 Speaker 3: would call the middle market, connecting the warehouse to the store. 95 00:05:17,000 --> 00:05:19,400 Speaker 3: And then you have Kodiak and Aurora, which all they 96 00:05:19,400 --> 00:05:21,520 Speaker 3: want to do is take it from warehouse to warehouse, 97 00:05:21,800 --> 00:05:24,680 Speaker 3: and those warehouses would be usually outside of the city, 98 00:05:24,839 --> 00:05:26,839 Speaker 3: right along a major highway, so they don't have to 99 00:05:26,839 --> 00:05:28,000 Speaker 3: go into city traffic. 100 00:05:28,839 --> 00:05:31,000 Speaker 1: Tom, give us a little tutorial here. I think we're 101 00:05:31,000 --> 00:05:33,719 Speaker 1: all familiar with cameras would be but what's the difference 102 00:05:33,760 --> 00:05:37,400 Speaker 1: between the two other technologies radar and light ar. 103 00:05:38,600 --> 00:05:41,520 Speaker 3: So radar they bounce off radio waves and if it 104 00:05:41,640 --> 00:05:45,040 Speaker 3: hits something, it will echo back and that way they 105 00:05:45,040 --> 00:05:48,880 Speaker 3: can see, Okay, there's something in that radio wave spectrum 106 00:05:48,920 --> 00:05:52,640 Speaker 3: out there. Light Ar is essentially the same type of concept, 107 00:05:52,720 --> 00:05:53,800 Speaker 3: except it's with light. 108 00:05:54,720 --> 00:05:57,200 Speaker 5: Some of them work better when it's raining, others need 109 00:05:57,240 --> 00:05:59,400 Speaker 5: more light. So they complement each other. 110 00:05:59,640 --> 00:06:02,240 Speaker 3: And then of course you have the cameras, which gives 111 00:06:02,279 --> 00:06:05,640 Speaker 3: you a visual. They talk about being able to have 112 00:06:05,920 --> 00:06:09,240 Speaker 3: sensors out to about one thousand meters, so you have 113 00:06:09,320 --> 00:06:14,760 Speaker 3: these three main modes constantly sweeping and picking up information 114 00:06:15,040 --> 00:06:17,719 Speaker 3: about every tenth of a second. They're going through a 115 00:06:17,760 --> 00:06:20,800 Speaker 3: cycle and feeding the computer information, so they have a 116 00:06:20,800 --> 00:06:23,440 Speaker 3: pretty good picture of what's going on around them. 117 00:06:24,160 --> 00:06:25,960 Speaker 1: If you're riding down the road and you see a 118 00:06:26,000 --> 00:06:28,000 Speaker 1: driverlest truck, does it look the same as a regular 119 00:06:28,040 --> 00:06:30,480 Speaker 1: eighteen wheel erd? Does it have all kinds of stuff 120 00:06:30,560 --> 00:06:31,800 Speaker 1: stuck on the outside. 121 00:06:32,360 --> 00:06:35,880 Speaker 3: Aurora has a sensor bar that goes on top of 122 00:06:35,880 --> 00:06:39,600 Speaker 3: the cab. Then it had these almost like cone shape things, 123 00:06:39,839 --> 00:06:41,960 Speaker 3: what would it be like a paint can size maybe 124 00:06:41,960 --> 00:06:44,960 Speaker 3: a little smaller sticking out on the top. And then 125 00:06:45,080 --> 00:06:48,400 Speaker 3: Kodiak has taken a different tact. They're putting their center 126 00:06:48,480 --> 00:06:49,400 Speaker 3: package in. 127 00:06:49,640 --> 00:06:51,920 Speaker 5: The area where the rear view mirror is. 128 00:06:52,279 --> 00:06:55,720 Speaker 3: So it looks like a small toolbox you might have 129 00:06:55,720 --> 00:06:58,039 Speaker 3: in your garage that's tipped up on its end, so 130 00:06:58,080 --> 00:07:00,800 Speaker 3: it's bigger than the normal rear mirror, and it does 131 00:07:00,839 --> 00:07:03,160 Speaker 3: have the mirror in there, but along with that has 132 00:07:03,160 --> 00:07:06,360 Speaker 3: a bunch of sensors and right now the Kodiak. All 133 00:07:06,400 --> 00:07:08,680 Speaker 3: of them have their names on them and some have 134 00:07:08,760 --> 00:07:11,400 Speaker 3: autonomous truck written across them, so if you know what 135 00:07:11,440 --> 00:07:12,640 Speaker 3: you're looking for, you can see them. 136 00:07:14,440 --> 00:07:16,800 Speaker 1: Time when you were reporting this story, you actually got 137 00:07:16,800 --> 00:07:19,560 Speaker 1: to get inside the cab of these trucks and ride 138 00:07:19,800 --> 00:07:21,360 Speaker 1: inside them. What was that like? 139 00:07:22,560 --> 00:07:23,360 Speaker 5: It was a lot of fun. 140 00:07:23,840 --> 00:07:25,880 Speaker 3: I'd love to do these type of things where you 141 00:07:25,880 --> 00:07:27,400 Speaker 3: get to go see something. 142 00:07:28,200 --> 00:07:29,239 Speaker 5: You have a safety driver. 143 00:07:29,520 --> 00:07:32,440 Speaker 3: It's usually a driver that's had a ton of experience, 144 00:07:32,480 --> 00:07:35,800 Speaker 3: and they picked the most safe drivers possible to recruit. 145 00:07:36,320 --> 00:07:39,640 Speaker 3: And then on the passenger seat of the truck there 146 00:07:39,680 --> 00:07:43,360 Speaker 3: will be another person who's taking all kinds of diagnostics 147 00:07:43,360 --> 00:07:45,720 Speaker 3: of how the system's working and if it had to 148 00:07:45,800 --> 00:07:48,640 Speaker 3: be engaged, and all these kinds of things that they 149 00:07:48,680 --> 00:07:52,000 Speaker 3: do to build their safety case. Essentially, as you take off, 150 00:07:52,040 --> 00:07:54,680 Speaker 3: the truck starts and the safety driver has his hands 151 00:07:54,800 --> 00:07:57,320 Speaker 3: covered over the wheel, but you can tell he's about 152 00:07:57,320 --> 00:07:58,880 Speaker 3: an inch or so away from it. 153 00:07:59,360 --> 00:08:01,160 Speaker 5: I was watching hands quite a lot. 154 00:08:01,200 --> 00:08:03,239 Speaker 3: I wanted to see, Okay, when we do a turn 155 00:08:03,400 --> 00:08:05,000 Speaker 3: or we do something, is he going to touch this 156 00:08:05,120 --> 00:08:07,920 Speaker 3: or grab this? But during the trip I did not 157 00:08:08,080 --> 00:08:10,680 Speaker 3: see that happen. I was impressed with that. And in 158 00:08:10,720 --> 00:08:14,360 Speaker 3: our particular case, we pulled out of the terminal, drove up. 159 00:08:14,280 --> 00:08:15,200 Speaker 5: A service road. 160 00:08:15,480 --> 00:08:17,320 Speaker 3: We're going up this service road and we come to 161 00:08:17,360 --> 00:08:18,040 Speaker 3: a stop sign. 162 00:08:18,360 --> 00:08:20,320 Speaker 5: The truck turns left. 163 00:08:20,560 --> 00:08:22,920 Speaker 3: And one of the impressive things is that we were 164 00:08:22,920 --> 00:08:27,920 Speaker 3: going over and overpass over the highway and it navigated 165 00:08:28,080 --> 00:08:32,000 Speaker 3: a stop light that didn't have a protected left turn 166 00:08:32,360 --> 00:08:36,000 Speaker 3: where you actually get the green arrow to turn and 167 00:08:36,240 --> 00:08:38,800 Speaker 3: you don't have to worry about oncoming traffic because they 168 00:08:38,880 --> 00:08:41,400 Speaker 3: have to stop. If you don't have that protected left 169 00:08:41,440 --> 00:08:43,600 Speaker 3: you have to wait for all those cars to clear 170 00:08:44,360 --> 00:08:46,520 Speaker 3: and then you have to go. And sometimes you might 171 00:08:46,559 --> 00:08:48,760 Speaker 3: have to make a pretty quick decision if there's traffic 172 00:08:48,840 --> 00:08:50,160 Speaker 3: and there's only a small space. 173 00:08:50,480 --> 00:08:52,600 Speaker 5: So this truck was able to navigate that. 174 00:08:52,679 --> 00:08:54,920 Speaker 3: It had the green light, and it waited and a 175 00:08:54,920 --> 00:08:57,800 Speaker 3: couple of cars came by, and it pulled out, got 176 00:08:57,840 --> 00:08:58,560 Speaker 3: on the highway. 177 00:08:58,960 --> 00:09:00,520 Speaker 5: We drove on the highway a bit. 178 00:09:01,120 --> 00:09:03,600 Speaker 3: These things are going to drive a couple of miles 179 00:09:03,720 --> 00:09:06,680 Speaker 3: per hour below the speed limit. They're going to stay 180 00:09:06,720 --> 00:09:09,559 Speaker 3: in the right lanes, they're going to prod along, and 181 00:09:09,600 --> 00:09:12,360 Speaker 3: they're probably not going to interact as much with vehicles 182 00:09:12,400 --> 00:09:15,040 Speaker 3: as some other trucks do. When they're trying to gain 183 00:09:15,080 --> 00:09:16,880 Speaker 3: a little bit on the traffic and so forth, they 184 00:09:16,960 --> 00:09:20,240 Speaker 3: might be changing lanes more often. The machine doesn't care 185 00:09:20,360 --> 00:09:23,040 Speaker 3: how long it takes to get to their destination. They're 186 00:09:23,080 --> 00:09:25,920 Speaker 3: just going to pulog along. There was an instance where 187 00:09:26,080 --> 00:09:29,640 Speaker 3: a vehicle was coming on the on ramp and the 188 00:09:29,720 --> 00:09:31,040 Speaker 3: truck had a decision. 189 00:09:31,040 --> 00:09:32,559 Speaker 5: It could pull over and give it space, or it 190 00:09:32,559 --> 00:09:33,200 Speaker 5: could slow down. 191 00:09:33,240 --> 00:09:35,600 Speaker 3: The truck decided to slow down because there were cars 192 00:09:35,720 --> 00:09:39,200 Speaker 3: that were coming up on its left faster. One of 193 00:09:39,240 --> 00:09:43,079 Speaker 3: the coolest experiences was at the four way stop to 194 00:09:43,200 --> 00:09:45,040 Speaker 3: turn back on the service road and get back to 195 00:09:45,080 --> 00:09:45,640 Speaker 3: the warehouse. 196 00:09:45,920 --> 00:09:47,200 Speaker 5: There was quite a few cars. 197 00:09:47,200 --> 00:09:49,680 Speaker 3: In fact, every one of the stop signs had a 198 00:09:49,760 --> 00:09:54,040 Speaker 3: line of cars, so people had to navigate this correctly. 199 00:09:53,679 --> 00:09:54,560 Speaker 5: And take their turn. 200 00:09:55,080 --> 00:09:57,800 Speaker 3: So the truck comes up, it stops, and they were 201 00:09:57,880 --> 00:10:01,040 Speaker 3: kind of explaining to me what it's doing, what's checking 202 00:10:01,080 --> 00:10:03,960 Speaker 3: everybody where its turn is. And the three cars went, 203 00:10:04,240 --> 00:10:08,600 Speaker 3: and then the truck lunged forward, and after it lunched forward, 204 00:10:08,800 --> 00:10:12,160 Speaker 3: it stopped, and then it did the turn, and they 205 00:10:12,280 --> 00:10:15,520 Speaker 3: explained to me that the truck was gaining its space. 206 00:10:15,679 --> 00:10:17,760 Speaker 3: In other words, it was saying, okay, it's my turn 207 00:10:17,840 --> 00:10:20,440 Speaker 3: with that lunge, and then when no other car moved, 208 00:10:20,600 --> 00:10:23,560 Speaker 3: the truck then proceeded. So these are the kind of 209 00:10:23,559 --> 00:10:26,880 Speaker 3: things that they have to program into this to mimic 210 00:10:27,160 --> 00:10:30,920 Speaker 3: how humans drive so that they can interact with vehicles 211 00:10:30,960 --> 00:10:32,760 Speaker 3: that have humans behind the wheel. 212 00:10:33,559 --> 00:10:35,920 Speaker 1: Did it feel kind of eerie to be in a 213 00:10:36,000 --> 00:10:38,040 Speaker 1: truck that was driving itself? 214 00:10:38,800 --> 00:10:41,040 Speaker 3: I would say it was actually mundane in a way 215 00:10:41,240 --> 00:10:43,800 Speaker 3: because you do have the safety driver there. You know, 216 00:10:43,880 --> 00:10:46,440 Speaker 3: he can grab the wheel at any time. It doesn't 217 00:10:46,559 --> 00:10:49,520 Speaker 3: spook you in that sense. It is cool to watch 218 00:10:49,559 --> 00:10:52,640 Speaker 3: when the truck makes a turn, and then after it 219 00:10:52,679 --> 00:10:56,600 Speaker 3: completes the turn, the wheel has to wind back as 220 00:10:56,640 --> 00:10:59,200 Speaker 3: the wheels straighten up, so that's when you can really 221 00:10:59,280 --> 00:11:02,199 Speaker 3: see that, oh hey, he's not grabbing this wheel at all. 222 00:11:02,960 --> 00:11:05,120 Speaker 3: I'll take a lot of video that because to me, 223 00:11:05,240 --> 00:11:09,560 Speaker 3: that's where the action is right watching this truck steer itself. 224 00:11:10,120 --> 00:11:12,760 Speaker 1: After the break. What these trucks have to do to 225 00:11:12,840 --> 00:11:25,080 Speaker 1: persuade the public that they're safe. Tom, you've mentioned the 226 00:11:25,160 --> 00:11:28,199 Speaker 1: names of these three companies. Tell us about them. These 227 00:11:28,240 --> 00:11:29,520 Speaker 1: are American companies. 228 00:11:30,160 --> 00:11:31,360 Speaker 5: They're American companies. 229 00:11:31,559 --> 00:11:35,360 Speaker 3: In fact, Gatique and Kodiak are based in Silicon Valley 230 00:11:35,800 --> 00:11:39,640 Speaker 3: and Aurora is officially based in Pittsburgh, but their founder, 231 00:11:39,760 --> 00:11:43,600 Speaker 3: Chris Ermson, is based in Silicon Valley, so they're tech companies. 232 00:11:44,480 --> 00:11:47,200 Speaker 3: The interesting thing is most of these guys know each other. 233 00:11:47,520 --> 00:11:52,559 Speaker 3: Google was an early mover in autonomous vehicle technology, and 234 00:11:52,920 --> 00:11:56,840 Speaker 3: Chris Ermson and Don Burnett, who's the CEO of Kodiak, 235 00:11:56,920 --> 00:11:58,240 Speaker 3: they both work there. 236 00:11:58,800 --> 00:12:00,440 Speaker 5: It's about software that's. 237 00:12:00,320 --> 00:12:04,000 Speaker 3: What makes the barrier to entry to this is somewhat low. 238 00:12:04,040 --> 00:12:06,080 Speaker 3: In that sense, you don't have to set up a 239 00:12:06,120 --> 00:12:09,720 Speaker 3: truck manufacturing facility to be able to get into the business. 240 00:12:09,760 --> 00:12:12,000 Speaker 5: It really is about the software. 241 00:12:12,040 --> 00:12:14,880 Speaker 1: All three of them are testing these trucks in Texas. 242 00:12:14,920 --> 00:12:18,959 Speaker 3: Why Texas, Texas was an early mover and setting up 243 00:12:19,160 --> 00:12:24,000 Speaker 3: the regulations for this to happen. They passed legislation that 244 00:12:24,200 --> 00:12:28,520 Speaker 3: set the groundwork to test and eventually operate autonomous. 245 00:12:28,040 --> 00:12:29,480 Speaker 5: Vehicles on the roads. 246 00:12:30,080 --> 00:12:32,360 Speaker 3: That gives a lot of certainty, So I think that's 247 00:12:32,360 --> 00:12:34,800 Speaker 3: one of the things that was attractive. The other is 248 00:12:34,800 --> 00:12:37,640 Speaker 3: that Texas is a very big freight market. In fact, 249 00:12:37,720 --> 00:12:40,960 Speaker 3: it's the second biggest freight market behind California and it 250 00:12:41,080 --> 00:12:44,760 Speaker 3: has a vast area. It has a big port in Houston, 251 00:12:45,200 --> 00:12:48,240 Speaker 3: has several ports. Also, it has Dallas, which is a 252 00:12:48,280 --> 00:12:51,360 Speaker 3: transportation hub because it's basically in the middle of the 253 00:12:51,360 --> 00:12:54,960 Speaker 3: country and it has lots of wide open flat spaces. 254 00:12:56,920 --> 00:12:59,240 Speaker 1: In time, you write that all of these ports and 255 00:12:59,320 --> 00:13:03,000 Speaker 1: transport hubbs make a really good testing ground because these 256 00:13:03,000 --> 00:13:06,320 Speaker 1: trucks can practice in real life conditions. 257 00:13:07,080 --> 00:13:07,600 Speaker 5: That's right. 258 00:13:07,760 --> 00:13:11,600 Speaker 3: They start out with lots of simulation and doing things 259 00:13:11,880 --> 00:13:15,080 Speaker 3: in a test area. This is how they got their start. 260 00:13:15,280 --> 00:13:18,240 Speaker 3: They would be in confined area where they could test 261 00:13:18,280 --> 00:13:20,600 Speaker 3: the vehicles and make sure the software was doing what 262 00:13:20,720 --> 00:13:24,280 Speaker 3: it was supposed to and reading all the sensors correctly. 263 00:13:25,000 --> 00:13:28,800 Speaker 3: Once they got to a certain skill level, then they 264 00:13:28,800 --> 00:13:29,880 Speaker 3: had to take it to the road. 265 00:13:30,360 --> 00:13:31,320 Speaker 5: They've been doing. 266 00:13:31,080 --> 00:13:35,280 Speaker 3: This for several years now and they're getting close to saying, hey, 267 00:13:35,320 --> 00:13:39,040 Speaker 3: we're ready. In the case of Aurora, they've came out 268 00:13:39,080 --> 00:13:41,439 Speaker 3: and made a pretty bold statement that said that our 269 00:13:41,480 --> 00:13:45,360 Speaker 3: software is ready. It can handle any of the events 270 00:13:45,400 --> 00:13:48,040 Speaker 3: that come up on the road. The truck can handle 271 00:13:48,360 --> 00:13:51,880 Speaker 3: all the circumstances that it's going to phase. They've said 272 00:13:52,120 --> 00:13:54,720 Speaker 3: that case is closed. Now we're going to present our 273 00:13:54,760 --> 00:13:57,960 Speaker 3: safety case. Kodiak hasn't made that statement. They say that 274 00:13:58,000 --> 00:14:00,520 Speaker 3: they're there, but they're doing things a little bit differently. 275 00:14:00,840 --> 00:14:02,880 Speaker 3: That's kind of the progression I think we're going to see. 276 00:14:03,040 --> 00:14:05,160 Speaker 3: They're testing them on the road right now, they still 277 00:14:05,160 --> 00:14:07,880 Speaker 3: have the safety drivers. At some point they're going to 278 00:14:07,880 --> 00:14:10,800 Speaker 3: have to take the safety drivers out. I think the 279 00:14:10,840 --> 00:14:13,520 Speaker 3: people are going to want to see more information about 280 00:14:13,559 --> 00:14:14,720 Speaker 3: how many times. 281 00:14:14,360 --> 00:14:16,400 Speaker 5: These safety drivers actually grab the wheel. 282 00:14:16,640 --> 00:14:19,640 Speaker 3: They don't publish those, they say they give that information 283 00:14:19,720 --> 00:14:22,400 Speaker 3: to authorities, but I think at some point they're going 284 00:14:22,440 --> 00:14:24,160 Speaker 3: to have to bring a lot of information to the 285 00:14:24,200 --> 00:14:26,920 Speaker 3: public to say this is why we know these things 286 00:14:26,960 --> 00:14:28,120 Speaker 3: are safe. 287 00:14:28,160 --> 00:14:31,520 Speaker 1: This question of safety you report is really the big 288 00:14:31,600 --> 00:14:35,240 Speaker 1: one that trying to persuade both regulators and normal people 289 00:14:35,320 --> 00:14:38,040 Speaker 1: driving on the road that these things are safe. 290 00:14:38,920 --> 00:14:42,000 Speaker 3: I think I can make as bold a statement to 291 00:14:42,040 --> 00:14:45,200 Speaker 3: say that if they don't make the roadways safer, the 292 00:14:45,280 --> 00:14:46,240 Speaker 3: industry will fail. 293 00:14:46,760 --> 00:14:47,560 Speaker 1: And why is that? 294 00:14:48,320 --> 00:14:51,760 Speaker 3: It's almost a taboo thing. We do not want machines 295 00:14:52,240 --> 00:14:55,160 Speaker 3: that can hurt people. I think most people would agree 296 00:14:55,200 --> 00:14:57,360 Speaker 3: with me on that, But that's a pretty low bar 297 00:14:58,160 --> 00:15:02,520 Speaker 3: because on our roadways, forty thousand people die every year. 298 00:15:02,560 --> 00:15:07,720 Speaker 3: That includes everything passenger vehicles. That is an alarmingly high number. 299 00:15:07,800 --> 00:15:12,480 Speaker 3: And people have absorbed that information. They've decided they can 300 00:15:12,520 --> 00:15:14,320 Speaker 3: take the risk, and they get in their vehicles every 301 00:15:14,360 --> 00:15:17,640 Speaker 3: day and they drive. And as far as large trucks, 302 00:15:18,080 --> 00:15:21,760 Speaker 3: five thousand people die in accidents that involve large trucks, 303 00:15:21,880 --> 00:15:25,200 Speaker 3: and most of those people are the passenger car drivers 304 00:15:25,560 --> 00:15:28,840 Speaker 3: just because the truck is much bigger, and the vast 305 00:15:28,840 --> 00:15:32,000 Speaker 3: majority of the time the truck driver is not at fault. 306 00:15:32,200 --> 00:15:35,280 Speaker 3: It's usually a motorist that has done something silly or 307 00:15:35,440 --> 00:15:39,920 Speaker 3: something stupid and causes accidents. These autonomous vehicles are going 308 00:15:39,960 --> 00:15:43,080 Speaker 3: to have to be safer. I think that's clear, or 309 00:15:43,080 --> 00:15:45,120 Speaker 3: it's just not going to be accepted. 310 00:15:44,600 --> 00:15:45,320 Speaker 5: By the public. 311 00:15:46,120 --> 00:15:51,000 Speaker 3: Aurora, Kodiak, Gatique, all these companies know that they have 312 00:15:51,080 --> 00:15:54,640 Speaker 3: to be almost flawless, if not perfect, on the technology. 313 00:15:54,640 --> 00:15:58,360 Speaker 3: As far as safety goes. There are going to be accidents. 314 00:15:58,840 --> 00:16:02,200 Speaker 3: It's inevitable that there will be accidents, but the truck, 315 00:16:02,360 --> 00:16:04,840 Speaker 3: the system has to be able to show that it 316 00:16:04,960 --> 00:16:06,720 Speaker 3: either wasn't our fault or it was some kind of 317 00:16:06,800 --> 00:16:10,440 Speaker 3: mechanical failure that does happen, but it wasn't because of 318 00:16:10,520 --> 00:16:15,040 Speaker 3: the autonomous driving technology. That is important for these companies 319 00:16:15,280 --> 00:16:18,280 Speaker 3: maintain almost a spotless record where they can say, you 320 00:16:18,280 --> 00:16:22,200 Speaker 3: know what, our autonomous technology has never caused an accident. 321 00:16:23,320 --> 00:16:26,960 Speaker 1: So how has the safety record of these test trucks been. 322 00:16:27,080 --> 00:16:27,480 Speaker 1: So far? 323 00:16:28,400 --> 00:16:29,280 Speaker 5: So far, so good. 324 00:16:29,680 --> 00:16:32,080 Speaker 3: They haven't caused it an accident, that's for sure. They 325 00:16:32,080 --> 00:16:35,320 Speaker 3: have been in accidents. There is a reporting system, a 326 00:16:35,360 --> 00:16:40,280 Speaker 3: federal reporting system where they report any incident that has happened, 327 00:16:40,520 --> 00:16:43,360 Speaker 3: no matter how minor. The cool thing is that they 328 00:16:43,440 --> 00:16:46,680 Speaker 3: attached part of the police report to it in a 329 00:16:46,720 --> 00:16:48,600 Speaker 3: section so you can see what happened. 330 00:16:49,080 --> 00:16:49,800 Speaker 5: There was one. 331 00:16:49,640 --> 00:16:54,240 Speaker 3: Case where a vehicle crossed over two lanes and hit 332 00:16:54,280 --> 00:16:57,120 Speaker 3: the truck in the back, and it turned out that 333 00:16:57,240 --> 00:16:59,680 Speaker 3: the driver admitted that he fell asleep in his car, 334 00:17:00,000 --> 00:17:02,960 Speaker 3: lost over two lanes, but he wasn't hurt. It crumpled 335 00:17:03,000 --> 00:17:05,399 Speaker 3: up his hood, he had to be towed, but the 336 00:17:05,400 --> 00:17:08,320 Speaker 3: truck was able to carry on. I didn't see any 337 00:17:08,359 --> 00:17:10,760 Speaker 3: of the accident reports where you would say, oh, the 338 00:17:10,800 --> 00:17:13,359 Speaker 3: truck was that fault. Here it was the human essentially 339 00:17:13,440 --> 00:17:14,280 Speaker 3: that made the mistake. 340 00:17:15,160 --> 00:17:17,879 Speaker 1: Right now, these trucks are being tested in Texas, but 341 00:17:18,000 --> 00:17:20,800 Speaker 1: can they leave Texas and go into other states? 342 00:17:21,240 --> 00:17:25,280 Speaker 3: They can right now, the regulations have really been left 343 00:17:25,359 --> 00:17:29,560 Speaker 3: up to the states for autonomous trucking. The federal government 344 00:17:29,640 --> 00:17:32,720 Speaker 3: is likely to weigh in on this, but for right now, 345 00:17:33,000 --> 00:17:34,879 Speaker 3: it's really up to the states to allow this. 346 00:17:35,600 --> 00:17:38,480 Speaker 5: And there's a wide swath along the sun Belt, if 347 00:17:38,480 --> 00:17:40,800 Speaker 5: you will, of states. 348 00:17:40,119 --> 00:17:43,760 Speaker 3: That are allowing this, from Arizona, New Mexico, Texas on 349 00:17:43,960 --> 00:17:45,959 Speaker 3: out and even up into Oklahoma. 350 00:17:46,400 --> 00:17:48,120 Speaker 5: So, yes, it is happening. 351 00:17:48,600 --> 00:17:52,040 Speaker 1: Time you said that safety concerns have to be satisfied 352 00:17:52,040 --> 00:17:55,000 Speaker 1: before they can get final approval. How long until that happens. 353 00:17:55,000 --> 00:17:58,640 Speaker 1: How long do you think before we start seeing autonomous 354 00:17:58,680 --> 00:18:02,479 Speaker 1: eighteen wheelers all over the place in Texas and other states. 355 00:18:03,200 --> 00:18:06,440 Speaker 3: It's very interesting because in a state like Texas, there's 356 00:18:07,160 --> 00:18:11,199 Speaker 3: probably no other hurdles for Aurora, Kodiak and some of 357 00:18:11,240 --> 00:18:14,960 Speaker 3: these others to go driver out. It really kind of 358 00:18:15,080 --> 00:18:17,639 Speaker 3: is up to them to say, Okay, we've proved our 359 00:18:17,680 --> 00:18:18,320 Speaker 3: safety case. 360 00:18:18,720 --> 00:18:20,720 Speaker 5: They have more or less a green light to go. 361 00:18:20,880 --> 00:18:22,440 Speaker 3: I'm sure they're going to have to get some sign 362 00:18:22,440 --> 00:18:24,840 Speaker 3: off from state authorities when they want to go driver out, 363 00:18:24,880 --> 00:18:27,280 Speaker 3: because it's a big step. What's holding them back is 364 00:18:27,320 --> 00:18:29,480 Speaker 3: they know that they have to be really cautious about 365 00:18:29,480 --> 00:18:32,119 Speaker 3: this because we talked about that if they had an 366 00:18:32,119 --> 00:18:35,240 Speaker 3: accident in which the autonomous truck caused an accident, it 367 00:18:35,280 --> 00:18:40,360 Speaker 3: would be a terrible blow for the whole industry. 368 00:18:40,440 --> 00:18:43,359 Speaker 1: So when you look down the road, sorry for the pun, 369 00:18:43,960 --> 00:18:46,320 Speaker 1: five years, ten years, do you think that the highways 370 00:18:46,359 --> 00:18:49,359 Speaker 1: will be filled with more driverless trucks than trucks with 371 00:18:49,480 --> 00:18:50,600 Speaker 1: people behind the wheel. 372 00:18:51,320 --> 00:18:54,400 Speaker 3: I would probably say no, just because I think this 373 00:18:54,440 --> 00:18:57,280 Speaker 3: will be a slow rollout, slow and deliberate. 374 00:18:57,560 --> 00:18:58,359 Speaker 5: I could be wrong. 375 00:18:58,760 --> 00:19:03,120 Speaker 3: It doesn't have any any kind of production limitations. Again, 376 00:19:03,200 --> 00:19:06,280 Speaker 3: it's really about software. So if it does take off 377 00:19:06,280 --> 00:19:09,560 Speaker 3: and people embrace it and it really shows that it's safe, 378 00:19:09,760 --> 00:19:12,199 Speaker 3: I could see it ramping up quite quickly. But I 379 00:19:12,359 --> 00:19:14,840 Speaker 3: tend to think that it's going to be a slow rollout. 380 00:19:15,160 --> 00:19:16,520 Speaker 1: Tom, thanks for the ride along. 381 00:19:16,800 --> 00:19:17,720 Speaker 5: Yeah, it's been fun. 382 00:19:18,840 --> 00:19:21,600 Speaker 1: When we come back, the CEO of a driverlest trucking 383 00:19:21,600 --> 00:19:24,000 Speaker 1: company talks about what it takes to put one of 384 00:19:24,040 --> 00:19:36,320 Speaker 1: these vehicles on the road. Now, let's hear from someone 385 00:19:36,359 --> 00:19:39,800 Speaker 1: who's working to put these trucks down the road. Chris 386 00:19:39,880 --> 00:19:43,639 Speaker 1: Ermson is CEO of Aurora, one of the companies. Tom 387 00:19:43,920 --> 00:19:47,160 Speaker 1: was talking about. Chris, what made you want to get 388 00:19:47,160 --> 00:19:48,160 Speaker 1: into this business. 389 00:19:48,880 --> 00:19:51,040 Speaker 4: Honestly, back in the day, I thought it was cool. 390 00:19:51,240 --> 00:19:52,640 Speaker 4: You know, I was a graduate student of the time 391 00:19:52,680 --> 00:19:55,240 Speaker 4: in Carnegie Mellen and we'd been working on some very 392 00:19:55,280 --> 00:19:56,240 Speaker 4: slow moving robots. 393 00:19:56,280 --> 00:19:57,159 Speaker 5: We're out in the desert in. 394 00:19:57,160 --> 00:20:00,119 Speaker 4: The auto Comma and the robot drove about thirty You 395 00:20:00,119 --> 00:20:01,800 Speaker 4: send me to a second which is how fast you 396 00:20:01,880 --> 00:20:06,160 Speaker 4: move if you have a walker? And my PhD advisor said, Hey, 397 00:20:06,160 --> 00:20:09,560 Speaker 4: this is this competition to go drive robots across the desert 398 00:20:09,600 --> 00:20:12,360 Speaker 4: at fifty miles an hour. And I thought that sounds amazing. 399 00:20:13,080 --> 00:20:16,520 Speaker 4: And then over the last twenty years of working on it, 400 00:20:16,680 --> 00:20:18,879 Speaker 4: you know, I've had the chance to work with amazing people. 401 00:20:19,160 --> 00:20:22,879 Speaker 4: We work on a really challenging technical problem and a 402 00:20:22,920 --> 00:20:27,000 Speaker 4: problem that has profound impact on society, and so that's 403 00:20:27,040 --> 00:20:28,880 Speaker 4: kind of kept me with it over a couple of decades. 404 00:20:29,440 --> 00:20:32,480 Speaker 1: So those challenging technical problems, let me just talk about 405 00:20:32,480 --> 00:20:35,080 Speaker 1: some of those. What is the biggest hurdle that you've 406 00:20:35,160 --> 00:20:37,280 Speaker 1: had to try to puzzle through and overcome. 407 00:20:38,080 --> 00:20:40,840 Speaker 4: The joy of this problem is there's no one right that. 408 00:20:40,920 --> 00:20:45,159 Speaker 4: There's a collection of problems, you know, around how do 409 00:20:45,200 --> 00:20:47,480 Speaker 4: you see far enough down the road how do you 410 00:20:48,359 --> 00:20:51,080 Speaker 4: model the way other people are going to behave on 411 00:20:51,119 --> 00:20:53,760 Speaker 4: the road given what you do and how that interaction 412 00:20:53,880 --> 00:20:57,320 Speaker 4: works and the complexities of that, And then how do 413 00:20:57,359 --> 00:21:00,720 Speaker 4: you convince yourself that you know it works well enough 414 00:21:01,000 --> 00:21:03,040 Speaker 4: that you could trust it out in the world and 415 00:21:03,080 --> 00:21:06,000 Speaker 4: that when something breaks that it's going to respond in 416 00:21:06,000 --> 00:21:09,159 Speaker 4: a way that's safe. And so it's that collection of 417 00:21:09,200 --> 00:21:11,400 Speaker 4: all of those things, and then you layer on top 418 00:21:11,480 --> 00:21:15,679 Speaker 4: of the technology, the business challenges, and the engaging with 419 00:21:15,760 --> 00:21:18,760 Speaker 4: the public and policy makers, and you know, it's a 420 00:21:18,760 --> 00:21:19,639 Speaker 4: fascinating space. 421 00:21:20,480 --> 00:21:22,640 Speaker 1: Where are you right now? How many trucks are out 422 00:21:22,720 --> 00:21:23,920 Speaker 1: on the road, Where are they going? 423 00:21:24,520 --> 00:21:26,840 Speaker 4: So we're on the road today in Texas. We drive 424 00:21:26,880 --> 00:21:29,600 Speaker 4: between Fort Worth and Opasso and between Dallas and Houston. 425 00:21:30,400 --> 00:21:33,720 Speaker 4: Every day. We're hauling loads for customers. We work with 426 00:21:33,880 --> 00:21:37,840 Speaker 4: partners like FedEx, Wernerschneider, Hirschbach, Uber Freight, and a couple 427 00:21:37,840 --> 00:21:40,240 Speaker 4: of others we can't talk about yet, and we have 428 00:21:40,480 --> 00:21:42,600 Speaker 4: on order about thirty trucks that are out there. 429 00:21:43,119 --> 00:21:46,080 Speaker 1: Now. Are these trucks that are actually out there delivering 430 00:21:46,320 --> 00:21:49,840 Speaker 1: goods completely driverless or is there someone in the cab 431 00:21:49,920 --> 00:21:51,960 Speaker 1: with their hands kind of hovering over the steering wheel. 432 00:21:52,720 --> 00:21:56,440 Speaker 4: So today we have a team in the vehicles monitoring 433 00:21:56,440 --> 00:21:58,800 Speaker 4: the system, but the vast majority of the time it's 434 00:21:58,880 --> 00:22:02,160 Speaker 4: driving itself. By the end of next year, we expect 435 00:22:02,160 --> 00:22:04,080 Speaker 4: to be at the point where we have vehicles operating 436 00:22:04,080 --> 00:22:07,480 Speaker 4: on the road without people on board, so truly without 437 00:22:07,560 --> 00:22:08,520 Speaker 4: human operators there. 438 00:22:09,240 --> 00:22:12,760 Speaker 1: And what has to happen between now and then for 439 00:22:12,920 --> 00:22:15,800 Speaker 1: you to feel comfortable or anyone else who might want 440 00:22:15,840 --> 00:22:17,800 Speaker 1: to sign off on this, to say, yeah, we're totally 441 00:22:17,800 --> 00:22:19,880 Speaker 1: fine with these things just going off and doing their thing. 442 00:22:20,720 --> 00:22:23,679 Speaker 4: We've laid out a sequence of milestones. There were, you know, 443 00:22:23,720 --> 00:22:25,520 Speaker 4: a bunch of things, but the three big ones were 444 00:22:25,520 --> 00:22:27,720 Speaker 4: to get to feature complete, to get to what we're 445 00:22:27,760 --> 00:22:30,760 Speaker 4: called the roor driver ready, and then to launch commercially. 446 00:22:31,240 --> 00:22:33,840 Speaker 4: And so feature complete we achieved at the beginning of 447 00:22:33,880 --> 00:22:36,240 Speaker 4: this year, and that really meant that all the parts 448 00:22:36,240 --> 00:22:39,920 Speaker 4: are there, they work, but we haven't yet convinced ourselves 449 00:22:40,000 --> 00:22:42,879 Speaker 4: that they're all the way correct and that we validated 450 00:22:42,880 --> 00:22:45,320 Speaker 4: that we're safe to go. By the end of this year, 451 00:22:45,440 --> 00:22:47,399 Speaker 4: when we hit this goal of e Royer Driver Ready, 452 00:22:47,840 --> 00:22:51,040 Speaker 4: our expectation is at that point the parts that we control, 453 00:22:51,200 --> 00:22:53,720 Speaker 4: the software, the way the sensors work each other, what's 454 00:22:53,760 --> 00:22:56,719 Speaker 4: on the computer. All of that that we have done, 455 00:22:56,840 --> 00:22:59,159 Speaker 4: all of the testing and all of the analysis to 456 00:22:59,160 --> 00:23:01,720 Speaker 4: convince ourselves that if we had a truck that was 457 00:23:01,720 --> 00:23:03,680 Speaker 4: ready to go, we'd be happy to put it's on 458 00:23:03,760 --> 00:23:06,560 Speaker 4: the road and be safe. And then finally commercial launch 459 00:23:06,600 --> 00:23:08,560 Speaker 4: will happen next year. And that's where we've done the 460 00:23:08,680 --> 00:23:12,119 Speaker 4: work with our great automotive and truck partners to do 461 00:23:12,160 --> 00:23:14,200 Speaker 4: the final testing and integration to make sure our stuff 462 00:23:14,200 --> 00:23:16,040 Speaker 4: talks to their stuff the way it's supposed to. Everything 463 00:23:16,200 --> 00:23:16,960 Speaker 4: is copasetic. 464 00:23:19,080 --> 00:23:22,080 Speaker 1: Earlier, I was talking to my colleague Thomas Black, who 465 00:23:22,160 --> 00:23:24,840 Speaker 1: is saying that one of the challenges is that people 466 00:23:25,720 --> 00:23:28,560 Speaker 1: know that there will be accidents on the road people 467 00:23:28,560 --> 00:23:31,520 Speaker 1: are driving cars, but that the safety record, if it's 468 00:23:31,600 --> 00:23:35,200 Speaker 1: driven autonomously almost needs to be perfect or else people 469 00:23:35,320 --> 00:23:37,520 Speaker 1: will not have confidence. Is that like a bar that 470 00:23:37,600 --> 00:23:38,640 Speaker 1: you can meet. 471 00:23:39,000 --> 00:23:42,480 Speaker 4: It's easy to overlook the status quota today right that 472 00:23:42,680 --> 00:23:45,840 Speaker 4: forty two thousand Americans diner roads every year and want 473 00:23:45,880 --> 00:23:49,040 Speaker 4: to quarter million people by globally, and this is a 474 00:23:49,080 --> 00:23:53,080 Speaker 4: technology that really can drive that towards zero. And that's 475 00:23:53,119 --> 00:23:56,760 Speaker 4: what we're focused on, and that work we do to 476 00:23:56,880 --> 00:24:00,760 Speaker 4: demonstrate to ourselves that the vehicle is proficient, are profound. 477 00:24:01,280 --> 00:24:03,760 Speaker 4: There's a few different ways we've explored this. So one 478 00:24:03,880 --> 00:24:08,320 Speaker 4: is that the Department of Transportation has a taxonomy for 479 00:24:08,440 --> 00:24:12,720 Speaker 4: how vehicles get into collisions, and so we've taken that taxonomy. 480 00:24:12,800 --> 00:24:15,880 Speaker 4: They said, Okay, let's expose the euror driver to each 481 00:24:15,920 --> 00:24:18,639 Speaker 4: of these kind of scenarios, and then let's create variations 482 00:24:18,680 --> 00:24:21,840 Speaker 4: of them, tens of thousands of kind of near miss 483 00:24:21,840 --> 00:24:24,880 Speaker 4: events or near collision events, and let's make sure all 484 00:24:24,880 --> 00:24:26,800 Speaker 4: of them the eroor driver is behaving the way we'd 485 00:24:26,800 --> 00:24:29,800 Speaker 4: want to. That's events that you or I we'd see 486 00:24:29,840 --> 00:24:32,679 Speaker 4: if any one or two of these events in our lifetimes, 487 00:24:32,760 --> 00:24:36,200 Speaker 4: and we're pushing the system through tens of thousands of them. 488 00:24:36,440 --> 00:24:39,480 Speaker 4: And then we've actually looked at the real world implication 489 00:24:39,560 --> 00:24:42,320 Speaker 4: of this. So the road that we're driving between Dawnson 490 00:24:42,359 --> 00:24:45,280 Speaker 4: and Houston is I forty five. We've looked at all 491 00:24:45,359 --> 00:24:49,280 Speaker 4: of the fatal accidents that involved trucks that happened between 492 00:24:49,320 --> 00:24:51,360 Speaker 4: twenty eighteen and twenty twenty two, and we pulled the 493 00:24:51,400 --> 00:24:55,320 Speaker 4: incident reports on those, and then we created simulations where 494 00:24:55,359 --> 00:24:57,960 Speaker 4: we had the our driver operating the vehicle, and it 495 00:24:58,040 --> 00:24:59,880 Speaker 4: was twenty nine of these that the ero driver could 496 00:24:59,880 --> 00:25:02,880 Speaker 4: have been operating, and across all twenty nine of them, 497 00:25:02,920 --> 00:25:05,399 Speaker 4: if the row driver had been operating, the collision just 498 00:25:05,400 --> 00:25:07,359 Speaker 4: would not have occurred. And if you think about the 499 00:25:07,400 --> 00:25:10,920 Speaker 4: impact for those families of what this would mean, right 500 00:25:10,960 --> 00:25:14,320 Speaker 4: the not losing those loved ones, it's really the message 501 00:25:14,320 --> 00:25:14,800 Speaker 4: home for me. 502 00:25:15,560 --> 00:25:17,639 Speaker 1: I imagine you spend a certain amount of your time just 503 00:25:17,680 --> 00:25:20,240 Speaker 1: trying to explain this stuff to people, to build a 504 00:25:20,400 --> 00:25:23,720 Speaker 1: kind of comfort level with the idea of this happening. 505 00:25:23,800 --> 00:25:26,880 Speaker 1: What's the biggest missingconception you've come across when you start 506 00:25:26,920 --> 00:25:29,520 Speaker 1: talking to people about a driverless truck. 507 00:25:30,440 --> 00:25:34,120 Speaker 4: People have rational concern about the new and the uncertain. 508 00:25:34,400 --> 00:25:38,640 Speaker 4: It's a perfectly normal behavior and expected and warranted. We're 509 00:25:38,680 --> 00:25:41,359 Speaker 4: driving seventy thousand pound trucks down the road. You should 510 00:25:41,440 --> 00:25:45,480 Speaker 4: have questions. And what we find is that people very 511 00:25:45,600 --> 00:25:48,960 Speaker 4: quickly accept. So I've been working this space for twenty 512 00:25:49,000 --> 00:25:52,399 Speaker 4: years and we've given folks rights in automated vehicles for 513 00:25:52,440 --> 00:25:54,600 Speaker 4: a better part of that time. You know, you'll have 514 00:25:54,600 --> 00:25:56,240 Speaker 4: the folks who are really excited about this, and so 515 00:25:56,320 --> 00:25:57,800 Speaker 4: let's not talk about that they get in this is 516 00:25:57,800 --> 00:26:00,840 Speaker 4: wonderful and amazing wow. Right, But you have the skeptics 517 00:26:01,040 --> 00:26:03,040 Speaker 4: and they're like, I don't know, this must be smoking mirrors. 518 00:26:03,080 --> 00:26:05,080 Speaker 4: I don't know that I'd ever trust it. And they 519 00:26:05,080 --> 00:26:07,399 Speaker 4: get in the back of the vehicle, and you know, 520 00:26:07,440 --> 00:26:09,879 Speaker 4: for the first five minutes they're kind of tense and 521 00:26:09,920 --> 00:26:12,600 Speaker 4: they're like, you know, what's happening here, right, And then 522 00:26:12,720 --> 00:26:15,080 Speaker 4: then they start to relax, and you know, invariably you 523 00:26:15,119 --> 00:26:17,479 Speaker 4: get some kind of common or question of it. Just 524 00:26:17,640 --> 00:26:20,320 Speaker 4: you know, it just drives, that's it. And then somewhere 525 00:26:20,320 --> 00:26:23,520 Speaker 4: between ten and fifteen minutes they're checking their phones and 526 00:26:23,600 --> 00:26:26,760 Speaker 4: so exposing people to this I think is one of 527 00:26:26,800 --> 00:26:30,000 Speaker 4: the ways that you can help them understand. And part 528 00:26:30,040 --> 00:26:33,120 Speaker 4: of my you know, amateur psychology here is that we're 529 00:26:33,160 --> 00:26:36,600 Speaker 4: all used to driving in vehicles that other people drive. 530 00:26:37,080 --> 00:26:39,439 Speaker 4: It's just a very common experience, and very quickly we 531 00:26:39,560 --> 00:26:42,560 Speaker 4: kind of pattern match to that and relax around it. 532 00:26:44,640 --> 00:26:47,399 Speaker 1: Chris, you've been at this for twenty years. I'm going 533 00:26:47,440 --> 00:26:49,960 Speaker 1: to ask you a crystal ball question. When you look 534 00:26:50,000 --> 00:26:54,560 Speaker 1: ahead ten years, what percentage of long haul trucks across 535 00:26:54,600 --> 00:26:56,639 Speaker 1: the US are autonomous. 536 00:26:57,160 --> 00:26:59,280 Speaker 4: It's still going to be a small percentage because the 537 00:26:59,280 --> 00:27:02,040 Speaker 4: market is just so big, right, It's an eight hundred 538 00:27:02,160 --> 00:27:06,040 Speaker 4: billion dollar market today, and so this technology is going 539 00:27:06,080 --> 00:27:07,919 Speaker 4: to roll out and grow and it's going to have 540 00:27:08,000 --> 00:27:12,200 Speaker 4: an impact, And initially it's going to be about filling 541 00:27:12,640 --> 00:27:15,000 Speaker 4: the shortage of drivers that are out there today. So 542 00:27:15,040 --> 00:27:17,679 Speaker 4: in the US we're short about eighty thousand drivers. We 543 00:27:17,760 --> 00:27:19,840 Speaker 4: expect by the end of the decade to be short 544 00:27:19,960 --> 00:27:22,480 Speaker 4: about twice that one hundred and sixty thousand, and so 545 00:27:22,880 --> 00:27:26,320 Speaker 4: automated driving is one way to compliment the human drivers 546 00:27:26,359 --> 00:27:28,480 Speaker 4: that are out there today doing a really important and 547 00:27:28,480 --> 00:27:29,080 Speaker 4: noble job. 548 00:27:30,119 --> 00:27:33,080 Speaker 1: Chris really enjoyed talking with you. Thanks for taking the time. 549 00:27:33,080 --> 00:27:34,440 Speaker 4: My pleasure. Really appreciate it. 550 00:27:35,240 --> 00:27:37,120 Speaker 1: Thanks for listening to us here at The Big Take. 551 00:27:37,240 --> 00:27:40,480 Speaker 1: It's a daily podcast from Bloomberg and iHeartRadio. For more 552 00:27:40,520 --> 00:27:44,600 Speaker 1: shows from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or 553 00:27:44,640 --> 00:27:47,440 Speaker 1: wherever you listen, and we'd love to hear from you. 554 00:27:47,480 --> 00:27:50,680 Speaker 1: Email us questions or comments to Big Take at Bloomberg 555 00:27:50,680 --> 00:27:54,119 Speaker 1: dot net. The supervising producer of The Big Take is 556 00:27:54,200 --> 00:27:58,400 Speaker 1: Vicky Bergolina. Our senior producer is Katherine Fink. Our producers 557 00:27:58,440 --> 00:28:02,720 Speaker 1: are Michael Falero and Mobero Raphael mcili is our engineer. 558 00:28:03,119 --> 00:28:06,920 Speaker 1: Our original music was composed by Leo Sidrin. I'm West Kasova. 559 00:28:07,240 --> 00:28:09,480 Speaker 1: We'll be back tomorrow with another big take.