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Autonomous and Semi-Autonomous Vehicles
An Introduction


Prof. David Bernstein
James Madison University

Computer Science Department
bernstdh@jmu.edu

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Society of Automotive Engineers (SAE) Levels
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  1. Driver Assistance - The vehicle contains some features that assist the driver
  2. Partial Automation - The driver must remain engaged and monitor at all times but the vehicle has some automated features (e.g., adaptive cruise control)
  3. Conditional Automation - The driver must be present and ready to take control with notice (i.e., needn't be monitoring the environment but must be monitoring the vehicle)
  4. High Automation - The vehicle is capable of performing all driving functions under some conditions
  5. Full Automation - The vehicle is capable of performing all driving functions under all conditions
Potential Benefits
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  • Safety:
    • Automated systems may make fewer errors than humans and, hence, be involved in fewer crashes and fewer serious crashes
  • Congestion and Capacity:
    • It may be possible to reduce congestion by smoothing traffic flows
    • It may be possible to increase capacity by decreasing safe following distances (a.k.a., platooning)
  • Mobility:
    • Many people currently can't drive (e.g., because of vision problems, seizure risks, etc.)
    • Autonomous transit vehicles could increase the supply of public transportation
Potential Benefits - Crash Reduction
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  • The Idea:
    • Eliminate human errors of various kinds
  • Limits:
    • The Insurance Institute for Highway Safety (IIHS) reviewed about 5000 crashes and divided them into the following errors: sensing and perceiving (24%), predicting, planning/deciding (40%), execution (20%), incapacitation including DWI (10%), unavoidable (2%), unknown (4%)
Potential Benefits - Platooning
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  • The Idea:
    • Improved sensing and decreased reaction times may decrease the safe following distance (e.g., from 1 vehicle length for every 10mph to 1 vehicle length for every 30mph)
  • Limits:
    • Maximum healthy deceleration rate
  • Using the Driver's Rule of Thumb:
    • images/Platooning_RuleOfThumb.png
History
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  • 1980s:
    • The Defense Advanced Research Projects Agency (DARPA) funded several projects
  • 1990s:
    • The National Automated Highway System (which considered vehicle+infrastructure technologies)
  • 2000s:
    • The DARPA Grand Challenges in 2004 (no successes), 2005 (5 successes), and 2007 (in an urban environment)
  • 2010s:
    • The private sector got much more interested
    • Several states started allowing the testing on public roads
Data
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  • About the Network:
    • Street network databases at a very high level of detail and accuracy (including speed limits, turn restrictions, etc.) are very useful
  • About Other Vehicles/Drivers:
    • Databases about vehicle and driver behavior may improve probabilistic predictive models
Sensing the Environment
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  • The Sensors Used:
    • Digital Cameras
    • LIDAR, RADAR and SONAR
  • Sensor Fusion:
    • Unlike with individual driver assistance technologies (e.g., lane departure, blind spot, collision avoidance systems), an autonomous vehicle system needs to be able to combine the data from all of the different sensors
  • Communication:
    • Connected vehicle networks may play an important role in the future, but currently do not
Algorithms
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  • Optimization and Optimal Control:
    • Used for the "simple" problems (e.g., route finding, acceleration/deceleration, steering adjustment)
  • Custom Algorithms and Heuristics:
    • Used for driving-specific problems (e.g., object avoidance, movement prediction)
  • Machine Learning and Artificial Intelligence:
    • Used for object (e.g., pedestrian, vehicle, traffic signal) identification and classification
    • Used for the "hard" problems (e.g., which pedestrian to avoid)
Nerd Humor
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http://imgs.xkcd.com/comics/self_driving.png
(Courtesy of xkcd)
Processing
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  • In the "Factory":
    • Large networks of computers can be used to train and test data off-line
  • In the Vehicle:
    • Traditional processors
    • Vector processors
    • Specialized processors
Enhancing the Infrastructure
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  • Lane/Road Markers:
    • Magnetic pins embedded in the pavement and/or special paints can facilitate sensing
  • Local Information:
    • Electronic street signs can eliminate/reduce the need for optical character recognition to determine "point" information
    • Over-the-air databases containing "area" information (e.g., about regulations, construction, etc.)
Some Unanswered Questions
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  • The Definition of Quality:
    • Should we just consider the outcomes (e.g., number of crashes) or the processes?
    • Should we just consider the "lagging" outcomes (e.g., number of crashes) or also "leading" outcomes (e.g., not creating hazards, responding to hazards)?
  • Ensuring Quality:
    • If outcomes matter, how should we collect the necessary data?
    • If processes matter, how should ML and AI algorithms be trained and tested?
  • Maintaining Quality:
    • Should algorithms be allowed to "learn" (i.e., adapt) after the vehicle leaves the factory
    • What should prompt a recall and what will the impacts be?
  • Liability:
    • Who is liable if something goes wrong?
More Nerd Humor
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http://imgs.xkcd.com/comics/driving.png
(Courtesy of xkcd)
There's Always More to Learn
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