  This paper presents algorithms for performing data-driven reachability
analysis under temporal logic side information. In certain scenarios, the
data-driven reachable sets of a robot can be prohibitively conservative due to
the inherent noise in the robot's historical measurement data. In the same
scenarios, we often have side information about the robot's expected motion
(e.g., limits on how much a robot can move in a one-time step) that could be
useful for further specifying the reachability analysis. In this work, we show
that if we can model this side information using a signal temporal logic (STL)
fragment, we can constrain the data-driven reachability analysis and safely
limit the conservatism of the computed reachable sets. Moreover, we provide
formal guarantees that, even after incorporating side information, the computed
reachable sets still properly over-approximate the robot's future states.
Lastly, we empirically validate the practicality of the over-approximation by
computing constrained, data-driven reachable sets for the
Small-Vehicles-for-Autonomy (SVEA) hardware platform in two driving scenarios.
