1. A theory on autonomous driving algorithms
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 System must be comfortable
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: This doesn’t quite work for our purposes
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?