Autonomous Vehicl cles Ethics cs & Law: A Mach chine Learning Trolley Problem? Tabrez Y. Ebrahim TEbrahim@cwsl.edu Source: Inventor Spot
– Tens of thousands of our people are being killed every year on our highways, hundreds of thousands are injured and all because the automobile is being driven by people who are not capable of turning their nervous systems and muscles into a perfect machine. – The Living Machine by David H. Keller, M.D. 1 The introduction of the new car as a driverless taxi was finally introduced. … Old people began to cross the continent on their own cars. Young people found the driverless car was admirable for petting. The blind for the first time were safe. Parents found that they could more safely send their children to school in the new car than in old cars with a chauffeur. … The new automatic automobile, the living machine was far more careful in its driving than the average moronic human chauffeur. – The Living Machine by David H. Keller, M.D. 2 In the 1935 short fiction book “The Living Machine”, writer David H. Keller wrote about This Article uses “autonomous vehicles” or AVs, which are considered synonymous with “driverless car” and “self car,” ™ “Level 0, ontrol the vehicle.”
Roadmap 1. Technological Background 2. Ethics 3. Trolley Problem 4. Decentralization
Technological Background Google
Technological Background 2.1.1. LIDAR LIDAR refers to a light detection and ranging device, which sends millions of light pulses per second in a well-designed pattern. With its rotating axis, it is able to create a dynamic, three-dimensional map of the environment. LIDAR is the heart for object detection for most of the existing autonomous vehicles. Figure 3 shows the ideal detection results from a 3D LIDAR, with all the moving objects being identified. Figure 3. The ideal detection result from a 3D LIDAR with all moving objects detected [22]. Scott Drew Pendleton et al.,, Perception, Planning, Control, and Coordination of Autonomous Vehicles
Different Levels of Automation in Autonomous Vehicles (AVs) SAE AUTOMATION LEVELS 1 1 2 3 4 0 5 Driver Assistance Partial Automation Conditional High Automation No Automation Full Automation Automation The driving mode- The driving mode- The driving mode - The full-time The full-time The driving mode - specifjc execution by specifjc execution by specifjc performance by performance by the performance by an specifjc performance by a driver assistance one or more driver an automated driving human driver of all automated driving an automated driving aspects of the dynamic system of either assistance systems system of all aspects system of all aspects system of all aspects of driving task, even when steering or acceleration/ of both steering of the dynamic driving of the dynamic driving the dynamic driving deceleration using or acceleration/ task, even if a human enhanced by warning or task under all roadway task with the expectation information about the deceleration using driver does not respond intervention systems. and environmental that the human driver driving environment and information about the appropriately to a conditions that can will respond with the expectation driving environment and request to intervene. be managed by a appropriately to a that the human driver with the expectation human driver. request to intervene. perform all remaining that the human driver aspects of the dynamic perform all remaining 1 SAE International, J3016_201806: Taxonomy and Defjnitions driving task. aspects of the dynamic for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (Warrendale: SAE International, driving task. 15 June 2018), https://www.sae.org/standards/content/ j3016_201806/. U.S. Department of Transportation, Automated Vehicles 3.0 Clear and consistent defjnition and use of terminology is critical to advancing the discussion around automation. To date, a document uses “automation” and “automated vehicles” as general terms to broadly describe the topic, with more specifjc language, such as “Automated Driving System” or “ADS” used when appropriate. A full glossary is in the Appendix.
Decision Making in AVs Navigation Camera(s) Navigation Data Provider/Service Vehicle to Vehicle People/Obstacles Laser Radar WiFi Communication Computing and Vehicle to Infrastructure Ultrasonic Sensor(s) mobile networks Decision Communication Making Other External Devices Earth/Geology Orientation Sensor(s) Bluetooth e.g., nearby phones Space/Satellites GPS Other External Services Act & control the vehicle Tobias Holstein et al., Ethical & Social Aspects of Self-Driving Cars
Comparison of How Humans & Computers “Learn” and “Interpret” Human Think & Sense Act Decide Learn from mistakes / misbehavior Computer Recognition & Sensor(s) & Computation & Act other Inputs Decision Making Feedback to manufacturer might change implementation, etc. Tobias Holstein et al., Ethical & Social Aspects of Self-Driving Cars
Should the expanding ability of AVs to make unsupervised decisions be “ethical”? Dieter Vanderelst & Alan Winfield, An Architecture for Ethical Robots Inspired by the Simulation Theory of Cognition
Ethics of Crashes § Traditional Ethical Theories • (1) Utilitarians (or consequentialists more broadly) • (2) Kantians (or deontologists more broadly) • (3) Virtue Ethics • (4) Contractulists Sven Nyholm, The Ethics of Crashes with Self-Driving Cars: A Roadmap
Ethics of Crashes § Who is the moral agent? Who needs to make the choice? • (1) person designing the car • (2) regulatory body permitting certain types of cars on the road • (3) car itself Sven Nyholm, The Ethics of Crashes with Self-Driving Cars: A Roadmap
“Machine Learning” § Use of Machine Learning by AVs • examining images taken as AV moves and makes comparisons to datasets of images • AV is not programmed with numerous if-then scenarios • instead, machine learning algorithm classifies images with high accuracy • assigning of a label to each image pixel and then building a classifier on top of that to predict an action
• • “sweet” • Machine Learning Crossroads Wolf Schafer, Ethical AI?: The Design of Moral Machines universe, humanity’s f
The Trolley Problem The consequences, in their most abstract sense, remain the same: Bryan Casey, Amoral Machines, Or: How Roboticists Can Learn to Stop Worrying and Love the Law
a collision (“inevitable collision state”) and the aim would be to minimize risk there will not be one “good” solution and the decision will involve a trade that is, “the capacity of reasoning on the perception and action in order to trivial choices” to the general domain of “robot ethics” whereas an AV’s AV Use Case: Someone Might be Harmed Figure 1. Sample use case Ebru Dogan et al., Ethics in the Design of Automated Vehicles: the AVEethics Project
AV Use Case: Someone Might be Harmed Figure 1: Three traffic situations involving imminent unavoidable harm. The car must decide between (a) killing several pedestrians or one passer by, (b) killing one pedestrian or killing its own passenger, (c) killing several pedestrians or killing its own passenger. Jean-Froncois Bonnefon, The Social Dilemma of Automated Vehicles
MIT’s Moral Machine [an open public platform for broader discussion of machine ethics] b What should the self-driving car do? Fig. 1 | Coverage and interface. a , World map highlighting the locations of Moral Machine visitors. Each point represents a location from which at least one visitor made at least one decision ( n = 39.6 million). The numbers of visitors or decisions from each location are not represented. b , Moral Machine interface. An autonomous vehicle experiences a sudden brake failure. Staying on course would result in the death of two elderly men and an elderly woman who are crossing on a ‘do not cross’ signal (left). Swerving would result in the death of three passengers: an adult man, an adult woman, and a boy (right). Edmond Awad et al., The Moral Machine Experiment
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