Inference Remapping for Vehicular Analytics - Bahl, Victor; Kandula, Srikanth; Patro, Ashish; Zarar, Shuayb (Microsoft, 2016)
A phone+car+cloud system can improve many vehicular scenarios significantly due to improved telemetry and the resulting optimizations. The core problem however is the inability to cope when inputs are missing or impossible to obtain apriori. We develop the concept of inference remapping which learns using correlations how to best use available substitutes for the missing inputs. We also describe an end-to-end system Sparc that combines an OBD device, a phone app and a cloud backend to drive a variety of applications. In particular, for the case of fuel usage prediction, we obtain a mechanical engineering theory based model that is accurate to within 2% when given ideal inputs (OBD data). We show how to remap the inference to only use phone data (7% error) or data available from a map (within 20% error for half the rides, which is 4 more accurate than state-ofart). A side-effect of our model is that we can offer detailed comparative feedback to drivers on their driving behavior.