Your comparison seems like a false dichotomy, and I think you are agreeing with OP. OP says, spend less time worrying about the algorithm and more time worrying about what data you are feeding the algorithm. You are saying, what if you had to choose between dataset A with algorithm A and dataset B with algorithm B.
You claim, (probably correctly) that dataset B, which includes velocity and trajectory, is more correct for the problem at hand, and given dataset B, I would suggest that either algorithm A or B would probably do just fine.
You also claim that algorithm A has "some error rate during identification." But so will algorithm B, and so will either algorithm on dataset A and B!
The question you should ask is, how much do I care about "black box" vs. "white box", and is there are trade-off? If the black-box solution (algorithm A, the "ML" solution) gives you 10% higher accuracy, and that accuracy is going to save lives, you bet I'd choose it. Or maybe I decide that interpretability is really important due to external audit reasons, so I need the white-box solution. But maybe I'd choose both, the interpretable one, and use the uninterpretable one as a flag for "a human should look at this." Or maybe I'd combine the results of both algorithms to get even higher accuracy.
There are just so many ways to configure a solution to the problem you propose, and you are only distinguishing between two of them. In the end the appropriate choice depends on context.
You claim, (probably correctly) that dataset B, which includes velocity and trajectory, is more correct for the problem at hand, and given dataset B, I would suggest that either algorithm A or B would probably do just fine.
You also claim that algorithm A has "some error rate during identification." But so will algorithm B, and so will either algorithm on dataset A and B!
The question you should ask is, how much do I care about "black box" vs. "white box", and is there are trade-off? If the black-box solution (algorithm A, the "ML" solution) gives you 10% higher accuracy, and that accuracy is going to save lives, you bet I'd choose it. Or maybe I decide that interpretability is really important due to external audit reasons, so I need the white-box solution. But maybe I'd choose both, the interpretable one, and use the uninterpretable one as a flag for "a human should look at this." Or maybe I'd combine the results of both algorithms to get even higher accuracy.
There are just so many ways to configure a solution to the problem you propose, and you are only distinguishing between two of them. In the end the appropriate choice depends on context.