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

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

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