Since we know
xianghua won't do this, I took a crack at it. It was fun.
First, I included a couple additional "taxa" including two types of Focus (sedan and coupe), two types of F-150 (XL and XLT), and finally a bicycle to act as a deliberate outlier.
I picked a bunch of arbitrary characteristics including:
1) Manufacturer logo/badge (i.e. Ford, Chevy, Honda, etc).
2) Number of wheels (2, 4 or >4)
3) Number of side doors (2 or 4)
4) Engine type (4, 6 or 8 cylinder)
5) Seating (driver, side passenger, rear passengers)
6) Rear hatch configuration (enclosed trunk, flip-down tail gate, lift-up hatch)
I put them into a binary character matrix (using Mesquite) and generated a tree accordingly. The tree was rooted using the bicycle.
I did have to make various assumptions. I stuck to the base model options as closely as possible using base features (lowest model engines, basic 2-door options for the pickup trucks, etc).
Here is the output:
Right away you can see some of the relationships are just odd. For example, the F-150 XLT has more in common with the Honda Accord than it does with the F-150 XL. Meanwhile the Mack Truck is most closely related to the F-150 XL.
Given the sheer number of options that vehicles have between different base models and trims, it's possible to get wildly different trees depending on what characteristics you change.
For example, here is a tree where I changed a single characteristic: I put a 6-cylinder engine in the Cadillac instead of the base 4-cylinder. Several of the relationships have changed, although I suspect this is primarily due to both limitations around the data set coupled with limitations on tree generation.
Choosing the underlying data set and tree search criteria, and the resultant number of possible valid trees that can result from it can have a significant impact on the output.
For example, here is another tree with the seating and door characteristics removed: