Model-based State Estimation of Two-Wheelers

Abstract

Comprehensive and correct state estimation with meaningful uncertainties is the basis of object-based perception for automated mobile platforms. According to fatality statistics, the most endangered group of vulnerable road users are single-track two-wheelers (ST2W), consisting mainly of cyclists, motorcyclists, and scooter riders. Due to counter-steering, they need more time to adjust their driving state to a new situation compared to four-wheelers that can directly steer in the desired direction without loosing balance. Therefore, the roll angle gives valuable information about possible future actions, especially for short, safety-critical prediction horizons. In this work, we present a basic, robust state estimation approach that is tailored to ST2W. Due to the lack of publicly available ST2W datasets with dynamic driving maneuvers and a highly accurate state, we recorded, labeled and published three different ST2W tracks ourselves. According to our results, the roll angle can be estimated bias-free with a standard deviation of between 4.4○to 6.4○, outperforming the chosen baseline.