The Poisson distribution assumes equal probability of events occurring. That seems to me to be an oversimplification, given that AV performance varies over time as changes are made, and also given that terrain / environment plays a huge factor here, whether looking at one particular vehicle or comparing to vehicles across companies (and drivers in general). Since AV performance will hopefully be improved when an accident occurs, we also cannot meet the assumption of independence between events. Although if AVs are simply temporarily stopped after an accident, that also breaks the independence assumption as we'd have a time period of zero accidents.
The bigger problem though is what you are doing with your confidence interval. A CI is a statement about replication. A 95% confidence level means that in 100 replications of the experiment using similar data, 5 of the generated CIs -- which will all have different endpoints -- will _not_ contain the population parameter, although IIRC this math is more complicated in practice, meaning that the error rate is actually higher. As such, if you generate a CI and multiply the endpoints by some constant, that's a complete violation of what is being expressed: there is vastly more data with 100m driving miles than 3m miles, which will cause the CI to shrink and the estimate of the parameter to become more accurate. There is absolutely no basis for multiplying the endpoints of a CI!
Ultimately, given that the size of the sample has an effect on CI width, you need to conduct an appropriate statistical test to compare the estimated parameters between the 1 in 3m deaths for Uber and whatever data generated the 1.18 in 100m deaths for sober drivers. There's a lot more that needs to be taken into account here than what a simple Poisson test can do.
For an analysis of how AVs with various safety levels perform in terms of lives saved over time, I recommend https://www.rand.org/blog/articles/2017/11/why-waiting-for-p...
Edit: Note the default values of the T and r parameters when you run poisson.test(1, conf.level = 0.95), and also that the p-value of the one-sample exact test you performed is 1. Also, since this is an exact test, the rate of rejecting true null hypotheses at 0.95 is 0.05, but given my reservations about the use of a Poisson distribution here, I don't think that using an exact Poisson test is appropriate.