An initial investigation into incorporating human reports into a road-constrained random set tracker

Abstract

Road-constrained tracking of multiple targets poses a challenge for standard tracking algorithms due to possible target/road ambiguities. The random set approach accepts the existence of ambiguity and tracks the probability density associated with each target/road hypothesis. Measurements from multiple sensors are used to update these densities via random set analogues of the Bayesian filtering equations. Reports from humans have the potential to complement and augment data provided by sensors. A challenge with incorporating human reports is that the reports' vagueness and ambiguity lead to many possible interpretations. We propose a method for incorporating human reports into a road-constrained random set tracker (RST). Our proposed approach involves mapping a human report into multiple plausible precise measurements. These precise measurements are used to update the global density in a manner similar to the sensor measurement case. We validated our approach using a simulated road network scenario, consisting of multiple sensors and targets and a simple human observer model. The human observer's reports contained coarse information about the number and relative location of the targets within a field of view. These human reports are mapped to multiple groups of plausible measurements consisting of ranges and bearing angles with large errors. The performance of the RST with and without the human reports is compared. A quantitative metric indicates that the inclusion of the human reports increases the belief of the RST in the correct target/road hypothesis.