Feature uncertainty estimation in sensor fusion applied to autonomous vehicle location

Within the complex driving environment, progress in autonomous vehicles is supported by advances in sensing and data fusion. Safe and robust autonomous driving can only be guaranteed provided that vehicles and infrastructure are fully aware of the driving scenario. This paper proposes a methodology for feature uncertainty prediction for sensor fusion by generating neural network surrogate models directly from data. This technique is particularly applied to vehicle location through odometry measurements, vehicle speed and orientation, to estimate the location uncertainty at any point along the trajectory. Neural networks are shown to be a suitable modeling technique, presenting good generalization capability and robust results.