The real-time simulation of human crowds has many applications. Simulating how the people in a crowd move through an environment is an active and ever-growing research topic. Most research focuses on microscopic (or 'agent-based') crowd-simulation methods that model the behavior of each individual person, from which collective behavior can then emerge. This state-of-the-art report analyzes how the research on microscopic crowd simulation has advanced since the year 2010. We focus on the most popular research area within the microscopic paradigm, which is local navigation, and most notably collision avoidance between agents. We discuss the four most popular categories of algorithms in this area (force-based, velocity-based, vision-based, and data-driven) that have either emerged or grown in the last decade. We also analyze the conceptual and computational (dis)advantages of each category. Next, we extend the discussion to other types of behavior or navigation (such as group behavior and the combination with path planning), and we review work on evaluating the quality of simulations. Based on the observed advancements in the 2010s, we conclude by predicting how the research area of microscopic crowd simulation will evolve in the future. Overall, we expect a significant growth in the area of data-driven and learning-based agent navigation, and we expect an increasing number of methods that regroup multiple 'levels' of behavior into one principle. Furthermore, we observe a clear need for new ways to analyze (real or simulated) crowd behavior, which is important for quantifying the realism of a simulation and for choosing the right algorithms at the right time.