Index

• A* algorithm, 82
• Actuators, 47, 191–192
• Adoption timeline, 18–21
• Adult recreation, 275, 276
• AI winters, 207, 208
• AlexNet. See Deep learning
• Artificial intelligence
  • Data-driven AI. See Machine learning
  • Rule-based AI, 7, 76
  • Rule-based AI, limitations of, 7, 8, 76, 88, 91–93, 154, 155
  • Symbolic AI. See rule-based AI
  • See also Artificial perception; Deep learning; Machine learning; Neural networks
• Artificial perception
  • Challenges with automating, 6, 85–88, 91, 92
  • Importance of, 7, 47, 85, 86
  • Template-based perception, 91
  • See also Artificial Intelligence; Machine vision; Mid-level controls; Moravec’s paradox; Object recognition; SuperVision
• Automation bias, 56
• Automotive control software. See Controls engineering
• Automotive industry
  • Competition with software companies, 46–55, 63
  • Driverless car impact on, 47, 52–55
  • Future of car design, 266–268
  • Incremental approach, 45
  • Industry insularity, 49–51
  • Possible strategies, 52–55
• Automotive operating system
  • Challenges of creating, 55–63, 68–75, 87–93, 98–102
  • Overview of, 47, 66, 67
  • See also Controls engineering; High-level controls; Low-level controls; Mid-level controls
• AUVSI conference, 47
• Aviation operating systems

• Backprop algorithm. See Error backpropogation
• Bar detectors. See Edge detectors
• Bel Geddes, Norman, 110, 111
• Bengio, Yoshua, 224
• Business models, 263–272
• Butterfly effect, 244

• Caltech-101, 219
• Cambrian Explosion, 9, 283
• CAN bus, 192–195
• Car industry. See Automotive industry
• Carnegie Mellon University (CMU), 68–72, 151, 157, 166, 274. See also NREC
• CHIMP, 69–71
• Cities. See Downtowns
• Closed world, 5
• Cloud robotics, 285
• CMU. See Carnegie Mellon University
• CNNs. See Convolutional Neural Networks
• Commuting, 38–40
• Computer operating systems
  • Operating system failure, 98–100, 104
• Cone of uncertainty, 95–97. See also Mid-level controls
• Consumer acceptance, 11–13
• Controls engineering
  • Overview of, 47, 75–77
  • See also Low-level controls; Mid-level controls; High-level controls
• Convolutional neural networks (CNNs), 214–218
• Corner cases, 4, 5, 89, 154
• Creative destruction, 261–263
• Crime, 273, 274

• DARPA Challenges, 149, 150
• DARPA Grand Challenge 2004
• DARPA Grand Challenge 2005, 151, 152
• DARPA Urban Challenge 2007, 156–158
• Data
  • CAN bus protocol, 193, 194
  • Data collection, 239, 240
  • Training data for deep learning, 218–220
  • See also Machine learning; Route-planning software; Traffic prediction software
• Deep learning
  • History of, 197, 199–202, 219, 223–226
  • How deep learning works, 7, 8, 226–231
  • See also ImageNet competition; Neocognitron; Perceptron; SuperVision
• Demo 97, 134, 135
• Digital cameras, 173–175
• Disney Hall, Los Angeles, 36
• Disney’s Magic Highway U.S.A.
• Dog of War, 79
• Downtowns, 32–37
• Drive by wire191–194
• Driver assist, 55–58. See also Human in the loop
• Driverless-car reliability, 98–104, 195–196
• Drive-PX 225

• E-commerce, 271, 272
• Edge detectors 229
• Electronic Highway
  • History of, 116–120
  • Reasons for demise, 123, 124
  • See also General Motors Corporation (GM)
• Environment. See Platooning; Traffic congestion; Traffic prediction software; Vehicular lifespan
• Error backpropagation. 211–213
• Ethics, 249–253
• Exponential technologies, 183, 255. See also Moore’s law

• Face recognition, 233
• Federal Highway Administration (FHA), 129, 132, 133
• Federal policy on autonomous vehicles
  • History of, 131–135
  • Overview of, 102, 103, 127
  • Recommendations for
  • See also Humansafe ratings; Intelligent Transportation Systems; U.S. Department of Transportation (USDOT); V2X
• FHA. See Federal Highway Administration
• Firebird, 121–122
• Fleet learning, 102, 240
• Fuel efficiency, 29, 30
• Fukushima, Kunihiko, 214
• Futurama, 107–110

• General Motors Corporation (GM), 107–110, 116–120
• Global positioning system (GPS), 10, 185–187
• GM. See General Motors Corporation
• Google
  • Google’s car accidents, 62, 63
  • Google’s Chauffeur project, 48, 58, 62, 102, 168–169
  • Google’s HD maps, 237, 238
  • Google’s self-driving prototype, 45, 77
• Government oversight. See Federal policy on autonomous vehicles)
• GPS. See Global positioning system
• GPUs. See Graphics processing units
• Graphic processing units (GPUs), 221, 222

• Hackers, 99, 195, 249, 274
• Handoff problem, See also Human in the loop, 57, 58, 62, 63
• Hardware sensors, 171–185. See also Digital cameras; High-definition digital maps; IMU; LIDAR; Radar sensor
• Hebb, Donald, 201
• Hebbian learning, 201
• Herculano-Houzel, Suzana, 73
• High-definition digital maps, 11, 171–173, 238, 239
• High-level controls, 75, 76, 81, 82
• Highway infrastructure
  • Dumb highways, 141–143
  • See also Electronic Highways
• Hinton, Geoffrey, 224
• Hubel, David, 229
• Human in the loop, 55–62
• Humansafe rating, 102–104

• ILSVRC. See ImageNet Large Scale Visual Recognition Competition
• ImageNet, 219, 220
• ImageNet Large Scale Visual Recognition Competition (ILSVRC), 223–225
• IMU. See Internal measurement unit
• Industrial revolution. See Zero Principle
• Insurance, 264, 265
• Intelligent Transportation Systems, 131–135
• Internal measurement unit (IMU), 187–191
• Invariant representation, 89

• Jobs, 259, 260

• Kornhauser, Alain, 138
• Krizhevsky, Alex, 224

• LeCun, Yann, 224
• Li, Fei-Fei, 219
• Lidar, 177–180
• Loneliness, 40–43
• Low-level controls, 75, 76

• Machine decision-making. See Ethics
• Machine intelligence. See Artificial intelligence
• Machine learning
  • Driving, applying machine-learning to, 152–153
  • History of, 158–163, 202–219
  • How machine learning works, 161–164
  • See also Deep learning
• Machine vision
  • Challenges with automating, 6, 72–75, 85–88, 91–92
  • History of, 6, 7
  • See also Artificial perception
• Marketing, 268, 269
• Mean distance between failures, 101. See also Humansafe rating
• Mean time between failures (MTBF), 101. See also Humansafe rating
• Mid-level controls, 86–87, 93–98
• Mid-level software. See Mid-level controls
• Minsky, Marvin, 208–210
• Moore’s Law, 10, 166
• Moravec’s paradox, 72–74
• Mortality rates from automobiles
• Municipal zoning, 33–35
• Musk, Elon, 11, 85–86
• Myths, 16–18

• National Automated Highway System Consortium (NAHSC), 133–135. See also Intelligent Transportation Systems
• National Highway Transportation and Safety Administration (NHTSA), 128
• Neocognitron, 214–216
• Neural networks, 199
  • Academic politics, 198, 199, 208–210
  • History of neural networks, 201–203, 210–213
  • How neural networks work, 199–201
  • See also Perceptron
• Ng, Andrew, 224
• NHTSA. See National Highway Transportation and Safety Administration
• 1964 World’s Fair, 121
• 1939 World’s Fair, 107–110
• NREC, 68–72
• NVIDIA, 225

• Object recognition, 87–90. See also Artificial perception
• Occupancy grid, 11, 93–95. See also Mid-level controls
• Operating systems. See Artificial Intelligence; Automotive operating system; Controls engineering; Robotic operating system

• Parking lots, 32–37
• Perceptron, 202–206, 208, 209
• Platooning, 30
• Precision agriculture, 74
• Pratt, Gill, 183
• Privacy, 247, 248, 249
• Public transportation, 40, 41, 42

• Qualia, 231–233

• Radar, 181–184
• RCA, 116–120
• Reactive controls. See Low-level controls
• Rebound effect, 25
• Recombinant innovation, 166
• Reliability, 67, 98–104, 195. See also Humansafe ratings
• Residual learning 228
• Retail, 269–272
• Road capacity, 30
• Robotic density, 48
• Robotic operating system
  • Challenges of creating, 71–74
  • Overview of, 66, 67
  • See also CHIMP; Controls engineering
• Robot morality. See Ethics
• Rosenblatt, Frank, 202, 203, 206–209
• Route-planning software, 245, 246

• Safety
  • Death rates from car accidents, 13, 14
  • Defining acceptable levels of safety, 102–104
  • Hackers, 99, 195, 274
  • See also CAN bus; Humansafe ratings
• Scene understanding, 89. See also Moravec’s paradox
• Seven delaying myths, 16, 17, 18
• Sex. See Adult recreation
• Shakey the robot, 90, 91
• Short-term trajectory planner, See also Mid-level controls, 97
• Simultaneous Localization and Mapping (SLAM), 240–243
• SLAM. See Simultaneous Localization and Mapping
• Software companies versus car companies, 46–55, 63
• State space, 76, 165
• Sun, Jian, 225
• SuperVision, 224–227. See also Deep learning
• Sutskever, Ilya, 224

• Taxi drivers, 260
• Template-based perception, 91, 229, 230. See also Shakey the robot
• Templeton, Brad, 142–146
• Thrun, Sebastian, 152, 168
• Traffic congestion, 25–28
• Traffic prediction software
• Trolley problem. See Ethics
• Truckers, 259–263

• Uber, 68, 260
• Unemployment. See Jobs
• U.S. Department of Transportation (USDOT), 128–132

• V2I. See V2X
• V2V. See V2X
• V2X
  • Drawbacks of, 136–140
  • Overview of, 129, 130, 136
• Vehicular lifespan, 28, 29

• Werbos, Paul, 210, 213
• “Who to kill.” See Ethics
• Wiesel, Torsten, 229
• World’s Fair (New York, 1939), 107–110
• World’s Fair (New York, 1964), 121

• XOR problem, 208

• Yosinski, Jason, 232

• Zero Principle, 255–258. See also Business models