Self-Aware Traffic Route Planning

One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, timevarying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traf- fic. We have applied our algorithm to large-scale traf- fic route planning, and demonstrated that our self-aware route planner can more accurately predict future traf- fic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm.

Authors

• David Wilkie University of North Carolina at Chapel Hill
• Jur van den Berg University of Utah
• Ming Lin Independent Researcher
• Dinesh Manocha University of Maryland, College Park

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