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A Swarm of Cars

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  1. A Swarm of Cars A theory on autonomous driving algorithms

  2. Motivation • Automobile accidents is one of the top 10 killers of people in the US • More and more drivers join the roads each day—safety and efficiency is of primary concern • Travel would become less “expensive”

  3. Ideas • Several theory’s have been suggested on how to accomplish this task • The most prominent use a hierarchical scheme • A SWARM system could also prove useful

  4. Restrictions on Study • Referring only to highway driving • Vehicles using these methods have certain technologies: • Speed sensors • Road vs. off-road sensors • Acceleration rates and stopping speed must be available

  5. Needs • System must exceed today’s safety • Fewer collisions • System must be comfortable • A roller-coaster type ride would not be acceptable • System must be adaptable • Replacing every vehicle on the road is never an option

  6. The Answer from the Sea • Schools of Fish exhibit all the needs of our system naturally • Very, very, very, very rarely collide • Move in smooth motions when not under attack • Schools vary in size from 10’s to 1,000,000’s

  7. A little background… • Fish sense obstacles and other fish around them with the Lateral Line Sense • Works similar to our ear • We must mimic this with our technology

  8. But wait… • Fish schools have already been modeled by computers • Used mostly in Computer Graphics • Fish are modeled by using the Flocking behavioral model • My work has been on adapting this model to fit onto a freeway

  9. Three behaviors • There are three behaviors that a flocking SWARM unit exhibits • Separation • The tendency of a unit to move away from others • Alignment • The Tendency of a unit to point in the same direction as others • Cohesion • The Tendency of a unit to move towards others

  10. Separation • Simple function: • V = - mS(1/D)/N • V is the placement vector • D is the distance vector between the unit and the obstacle • N is the number of obstacles • m is a multiplier • This doesn’t quite work for our purposes

  11. Separation • Separation zone should not be static • Should be related to the stopping distance at a given speed • X = -V^2/2a + b for directly in front of the vehicle • Smaller for sides, area behind is irrelevent • X = sin(T)*-V^2/2a + b for 0 < T < 180, X=b otherwise

  12. Separation • Vehicles cannot turn around • Therefore positioning must be relative

  13. Alignment • Alignment zone should be similarly shaped • Larger • Alignment Algorithm: Vehicle angle is the average angle of all the units in the alignment zone • Again, doesn’t quite work for our purposes • Need to ignore vehicles traveling in the opposite direction • Need Time Delay

  14. Cohesion • Works in opposition to Separation algorithm • Should be the largest zone, and actively searching for new members to flock with • Need other members to share information • Movement vector is opposite of Separation: • V = mSD/N

  15. Emergent behavior • Every member of the flock “sees” what the members at the front “see” • Members move in unison • Members will avoid obstacles in the same motion

  16. Comparison • Vs. Hierarchical Network AHS • Pros: • Deployable as an “option” • No single point of failure • No tracking movements • Cons: • No effective way to avoid congestion

  17. Questions?