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Unmanned planetary landers to date have landed Â¿blindÂ¿; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain, which in turn constrains the scientific agenda of missions. Systems for automatic surface reconstruction and for hazard detection, mapping, and assessment are becoming mature. Before they can be put to practical use, it is essential to be able to characterize their performance for the purposes of scientific evaluation and their utility to engineers planning and designing landed missions. It is also important to be able to predict performance for a variety of scenarios. The evaluation metrics need to be simple enough to be readily comprehensible but still to capture the important relevant performance parameters. In this paper we describe the process, metrics, results, and algorithm improvement recommendations from the evaluation of the performance of the hazard detection and avoidance (HDA) algorithms developed in the Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project by means of Monte Carlo simulation of thousands of Lunar landings.