Autonomous Vehicles PP.ppt
- 1. Autonomous Vehicles By: Rotha Aing
- 2. What makes a vehicle autonomous? • “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
- 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
- 22. Path Analysis • Built in RDDF (database of course) • Vehicle predominantly followed the RDDF data
- 23. 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
- 25. Issues with mapping scheme • Errors in determining environment – 12.6% of areas determined as obstacle was not
- 26. Alice out of challenge
- 27. 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