See, this is what happens when you extrapolate data points linearly into the future. You get totally unrealistic predictions. It's important to remember the physical constraints on whatever trend you're trying to extrapolate. Importantly for this issue, you need to remember that time between successive crashes can never be negative, so it is inappropriate to model intervals with a straight line that crosses the time axis on April 7.

Instead, with so few data points, a more realistic model would take a log-transform of the inter-crash interval before fitting the prediction line. In fact, once you do so, it becomes clear that this is a geometric series, with inter-crash interval decaying exponentially with number of crashes. The total time taken for N cars to crash in front of your house after the first one grows as $T_N={\sum }_{n=1}^N{t}_0{r}^{n-1}=t_0\frac{1-r^N}{1-r}$, where $r\approx \frac{27}{155}\approx 0.174$ and $t_0=155$ days, based on your graph.

According to Google, there are 1.47 billion cars in the world. The time it will take for all of them to crash in front of your house is $T_{1.47e9}=155\frac{1-{0.174}^{1.47e9}}{1-0.174}=187.7$ days from the first crash, which works out to 5.7 days from today. Which turns out to be April 7.

Hmm...

Well, see you on Monday, I guess.

I accept your statistics and assume I'll be driving my car. Damn.

OTOH, I can be pretty certain I won't die or be seriously injured.

That has happened never in my thousands of weeks, so statistically, it almost certainly won't within the next week!

Quick! Someone fund my steel production startup before its too late! My business model is to place a steel foundry under your house to collect the exponentially growing amount of cars crashing into it! 
Imagine how much money we can make by revolutionizing metal production during the car crash singularity! Think of the money! Think of the Money! Think of the Money!!!