Read the case study and answer question Chapter 11 Managing Knowledge and Artificial Intelligence 453 Can Cars

Read the case study and answer question

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Chapter 11 Managing Knowledge and Artificial Intelligence 453 Can Cars Drive Themselves-And Should They? CASE STUDY W Will cars really be able to drive themselves without human operators? Should they? And are they good business investments? Everyone is searching for answers. A lidar fires out millions of laser beams every sec-a ond, measuring how long they take to bounce back. The lidar takes in a 360-degree view of a car's sur- roundings, identifying nearby objects with an accu- racy up to 2 centimeters. Lidars are very expensive Autonomous vehicle technology has reached a treme temperatures, rain, or snow. point where no automaker can ignore it, Every major and not yet robust enough for a life of potholes, ex- auto maker is racing to develop and perfect autono- mous vehicles, believing that the market for them could one day reach trillions of dollars. Companies such as Ford, General Motors, Nissan, Mercedes, Tesla, and others have invested billions in autono- mous technology research and development. Ford in- vested $1 billion in AI firm Argo AI, and GM bought a self-driving car startup called Cruise. Ford has set a goal of producing a self-driving car with no pedals by 2021. Ride-hailing companies like Uber and Lyft believe driverless cars that eliminate labor costs are key to their long-term profitability. Cars that drive themselves have been on the road in select loca- tions in California, Arizona, Michigan, Paris, London, Singapore, and Beijing. Waymo, the company that emerged from Google's self-driving car project, pre- dicts that by 2020 its fleet of self-driving Jaguars will make as many as one million trips per day. A car that is supposed to take over driving from a human requires a very powerful computer system that must process and analyze large amounts of data generated by myriad sensors, cameras, and other devices to control and adjust steering, accelerating, and braking in response to real-time conditions. Key technologies include: Sensors: Self-driving cars are loaded with sensors of many different types. Sensors on car wheels measure car velocity as it drives and moves through traffic. Ultrasonic sensors measure and track positions of line curbs, sidewalks, and objects very close to the car. Cameras: Cameras are needed for spotting things like lane lines on the highway, speed signs, and traffic lights. Windshield-mounted cameras create a 3-D image of the road ahead. Cameras behind the rear-view mirror focus on lane markings. Infrared cameras pick up infrared beams emitted from head- lamps to extend vision for night driving. Lidars: Lidars are light detection and ranging de- Ivices which sit on top of most self-driving cars. GPS: A global positioning system (GPS) pinpoints the car's macro location, and is accurate to within 1.9 meters. Combined with reading from tachom- mmodat eters, gyroscopes, and altimeters, it provides initial positioning. Radar: Radar bounces radio waves off of objects to help see a car's surroundings, including blind spots, and is especially helpful for spotting big metallic ob- jects, such as other vehicles. Computer: All the data generated by these tech- nologies needs to be combined, analyzed, and turned into a robot-friendly picture of the world, with in- nola structions on how to move through it, requiring almost supercomputer-like processing power. Its soft- ware features obstacle avoidance algorithms, predic- tive modeling, and "smart" object discrimination (for example, knowing the difference between a bicycle and a motorcycle) to help the vehicle follow traffic rules and navigate obstacles. Machine Learning, Deep Learning, and Computer Vision Technology: The car's computer system has to be "trained" using machine intelligence and deep learning to do things like detect lane lines and identify cyclists, by show- ing it millions of examples of the subject at hand. Because the world is too complex to write a rule for every possible scenario, cars must be able to "learn" from experience and figure out how to navigate on their own. Maps: Before an autonomous car takes to the streets, its developers use cameras and lidars to map its territory in extreme detail. That information helps the car verify its sensor readings, and it is key for any vehicle to know its own location. Self-driving car companies are notorious for over- hyping their progress. Should we believe them? At this point, the outlook for them is clouded. Chapter 11 Managing Knowledge and Artificial Intelligence 453 Can Cars Drive Themselves-And Should They? CASE STUDY W Will cars really be able to drive themselves without human operators? Should they? And are they good business investments? Everyone is searching for answers. A lidar fires out millions of laser beams every sec-a ond, measuring how long they take to bounce back. The lidar takes in a 360-degree view of a car's sur- roundings, identifying nearby objects with an accu- racy up to 2 centimeters. Lidars are very expensive Autonomous vehicle technology has reached a treme temperatures, rain, or snow. point where no automaker can ignore it, Every major and not yet robust enough for a life of potholes, ex- auto maker is racing to develop and perfect autono- mous vehicles, believing that the market for them could one day reach trillions of dollars. Companies such as Ford, General Motors, Nissan, Mercedes, Tesla, and others have invested billions in autono- mous technology research and development. Ford in- vested $1 billion in AI firm Argo AI, and GM bought a self-driving car startup called Cruise. Ford has set a goal of producing a self-driving car with no pedals by 2021. Ride-hailing companies like Uber and Lyft believe driverless cars that eliminate labor costs are key to their long-term profitability. Cars that drive themselves have been on the road in select loca- tions in California, Arizona, Michigan, Paris, London, Singapore, and Beijing. Waymo, the company that emerged from Google's self-driving car project, pre- dicts that by 2020 its fleet of self-driving Jaguars will make as many as one million trips per day. A car that is supposed to take over driving from a human requires a very powerful computer system that must process and analyze large amounts of data generated by myriad sensors, cameras, and other devices to control and adjust steering, accelerating, and braking in response to real-time conditions. Key technologies include: Sensors: Self-driving cars are loaded with sensors of many different types. Sensors on car wheels measure car velocity as it drives and moves through traffic. Ultrasonic sensors measure and track positions of line curbs, sidewalks, and objects very close to the car. Cameras: Cameras are needed for spotting things like lane lines on the highway, speed signs, and traffic lights. Windshield-mounted cameras create a 3-D image of the road ahead. Cameras behind the rear-view mirror focus on lane markings. Infrared cameras pick up infrared beams emitted from head- lamps to extend vision for night driving. Lidars: Lidars are light detection and ranging de- Ivices which sit on top of most self-driving cars. GPS: A global positioning system (GPS) pinpoints the car's macro location, and is accurate to within 1.9 meters. Combined with reading from tachom- mmodat eters, gyroscopes, and altimeters, it provides initial positioning. Radar: Radar bounces radio waves off of objects to help see a car's surroundings, including blind spots, and is especially helpful for spotting big metallic ob- jects, such as other vehicles. Computer: All the data generated by these tech- nologies needs to be combined, analyzed, and turned into a robot-friendly picture of the world, with in- nola structions on how to move through it, requiring almost supercomputer-like processing power. Its soft- ware features obstacle avoidance algorithms, predic- tive modeling, and "smart" object discrimination (for example, knowing the difference between a bicycle and a motorcycle) to help the vehicle follow traffic rules and navigate obstacles. Machine Learning, Deep Learning, and Computer Vision Technology: The car's computer system has to be "trained" using machine intelligence and deep learning to do things like detect lane lines and identify cyclists, by show- ing it millions of examples of the subject at hand. Because the world is too complex to write a rule for every possible scenario, cars must be able to "learn" from experience and figure out how to navigate on their own. Maps: Before an autonomous car takes to the streets, its developers use cameras and lidars to map its territory in extreme detail. That information helps the car verify its sensor readings, and it is key for any vehicle to know its own location. Self-driving car companies are notorious for over- hyping their progress. Should we believe them? At this point, the outlook for them is clouded.