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A modular framework for adaptive agent-based steering

Next-generation steering algorithms will need to support thousands of believable individual agents, capable of steering in very challenging situations with low-latency reactions. In this paper we propose a steering framework that offers three key contributions: (a) It integrates several models of steering into a single steering decision, (b) it employs a novel space-time planning approach to allow agents to steer during complex local interactions, and (c) it varies the frequency of update of each component (phase) of the framework to drastically improve performance. We demonstrate the versatility and robustness of our framework using a large number of test cases. We also show that the frequency of updates for each phase of the framework can be "decimated" by a surprisingly large amount before resulting steering behaviors degrade. This technique achieves more than a 5x performance improvement, allowing the use of better, more costly algorithms for robust steering, while supporting thousands of agents with low-latency reactions in real-time.