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This paper presents algorithms for performing data-driven reachabilityanalysis under temporal logic side information. In certain scenarios, thedata-driven reachable sets of a robot can be prohibitively conservative due tothe inherent noise in the robot's historical measurement data. In the samescenarios, 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 beuseful for further specifying the reachability analysis. In this work, we showthat if we can model this side information using a signal temporal logic (STL)fragment, we can constrain the data-driven reachability analysis and safelylimit the conservatism of the computed reachable sets. Moreover, we provideformal guarantees that, even after incorporating side information, the computedreachable sets still properly over-approximate the robot's future states.Lastly, we empirically validate the practicality of the over-approximation bycomputing constrained, data-driven reachable sets for theSmall-Vehicles-for-Autonomy (SVEA) hardware platform in two driving scenarios.

Enhancing Data-Driven Reachability Analysis using Temporal Logic Side Information