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Autonomous Vehicles

Autonomous Vehicles. By: Rotha Aing. What makes a vehicle autonomous ?. “Driverless” Different from remote controlled 3 D’s Detection Delivery Data-Gathering. 3D’s. Detection – Reasoning The surroundings and current conditions Data-gathering – Search

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Autonomous Vehicles

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  1. Autonomous Vehicles By: Rotha Aing

  2. What makes a vehicleautonomous? • “Driverless” • Different from remote controlled • 3 D’s • Detection • Delivery • Data-Gathering

  3. 3D’s • Detection – Reasoning • The surroundings and current conditions • Data-gathering – Search • From the information search knowledgebase for purposed actions • What to do next? • Delivery – Learning • View and record results of actions

  4. Current Approaches • Fully Autonomous • Taxi-like cars • Autonomous in closed systems • Monorails • Assistance System • Environment Sensing • Distance Sensors • ABS

  5. Solution Template • Sensors: Figure out obstacles around the vehicle • Navigation: How to get to the target location from the present location • Motion planning: Getting to the location, getting by any obstacles, following any rules • Control: Getting the vehicle itself to move

  6. Current Issues • Technical • Sensors • Understanding the environment • Navigation • Know its current position and where it wants to go • Motion Planning • Navigation through traffic • Actuation • Operate the correct and needed features

  7. Issues • Social Issues • Trusting the car • Getting on public roads • Getting people to go in • Liability Issues • Lost Jobs

  8. What’s been solved? • Control • Navigation • Some issues of Sensory

  9. Control • Drive-By-Wire • Sends messages to onboard computers • Physical ties are unlinked • In most current cars

  10. Drive By Wire • When sensor/trigger is pressed, it sends message to the car to perform the tasks

  11. DBW in Autonomous Vehicles • Replace the human driver • Activate the sensors/triggers • SciAutonics • Servomotors for each gear • Large servomotor with belt drive for steering

  12. Navigation • Already available • Combination of: • GPS • Roadside database

  13. Sensory • Major issue: • Lack of computing power • “More processors” • Half completed • RADAR • Laser Detection • Cameras

  14. Sensory Information Issues • Factors of weather • Dust, rain, fog • Correctly Identifying an obstacle • Shadows vs. ditches • Shallow vs. deep • Speed of the vehicle and the speed data can be correctly received

  15. Motion Planning • Most challenging • Collision Detection • Affected by: • Quality of Sensory information • Quality of Controls • Need for algorithm that can determine movements quickly but also the correct ones

  16. “Road Map” • Decision Tree (Graph) • With points A and G • Fill in free spots (Configuration Space) • Try to link A to G • Configuration Space Algorithms • Sampling-based • Faster, less computing power • Combinatorial • More complete

  17. Configuration Space

  18. DARPA Challenge • Defense Advanced Research Projects Agency • 2004 Desert Course • 2005 Off-road, mountain terrain • 2007 Urban Challenge • Collision Avoidance • Obey traffic signs

  19. Stanley • 2005 DARPA Challenge winner • Volkswagen Touareg modified with onboard computers

  20. Stanley’s Sensory • 5 LIDAR lasers • 24 GHz RADAR • Stereo camera • Single-lens camera

  21. Path Analysis • Built in RDDF (database of course) • Vehicle predominantly followed the RDDF data

  22. Obstacle Detection • Machine Learning Approach • Accuracy value of data is based on how human’s perform • Slows down when a path can not be found quickly • Grid of either occupied, free, or unknown spots

  23. Issues with mapping scheme • Errors in determining environment • 12.6% of areas determined as obstacle was not

  24. Alice out of challenge

  25. Personal Opinions • Good progress since the first challenge • Not until the 2007 challenge will we really know if a fully autonomous vehicle is possible in the near future • Other approaches more likely to be developed into mainstream before fully autonomous vehicles

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