This algorithm is primarily designed for live loops. It is possible to use this algorithm for race routes but it may take a very long time to simulate/optimize and future data (like weather) may not be as accurate. Because this is a live algorithm, it is best for short routes using immediate future data (a few hours in advance)
Goals/Sub-projects
Loop specifics (What actions for the driver to take and when)
Binary decision (on whether or not we should do the loop)
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Since we have data from different sources, the data points may not exactly have a similar/consistent format (ie. we may have weather data in 100m intervals and elevation data in 200m intervals). In such as case, we can just use piecewise linear interpolation to calculate missing data
Using piecewise linear interpolation works fine if we assume that the data is linear (which is for the most part) and assumes that the interpolation data points are close to each other
We can also use quadratic, but this adds complexity to the algorithm and isn’t significantly better unless the interpolation data points are far aparthttps://i.stack.imgur.com/Bv8L4.png
Most (if not all) data points represent the average or total value for the data point. For example, the elevation data for a segment of 1km will be the total elevation gain. Smaller segments/intervals will always yield more accurate data
Confidence and uncertain intervals?
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