Project Summary

There are two parts to this project:

1) the binary decision - confidence score on whether to take the loop or not

2) the loop specifics - how do we take the loop (speed, when to stop, etc.)

Goal

• We already have the pre-race determined efficiency scores for each loop; this model gives us another efficiency score which takes into account realtime data 

• If this model produces a similar result to the pre-determined efficiency scores, then we can follow through with the pre-determined loops we would take 

• If this model produces drastically different results from the pre-determined efficiency scores due to unforeseen factors, then can adapt and use the model with current real time data

Inputs

• Weather

• Manual data on current race conditions

• Traffic

• Road conditions

• Braking/traction data

• Driver input

• Steering angle 

• Pre-race loop model

Output

• Confidence score 

• Provided intervals (yes, no, and grey area for decision making)

• How fast, and for what distance

• When to break/stop