A road profile can be a good reference feature for vehicle localization when a Global Positioning System signal is unavailable. However, cost effective and compact devices measuring road profiles are not available for production vehicles. This paper presents a longitudinal road profile estimation method as a virtual sensor for vehicle localization without using bulky and expensive sensor systems. An inertial measurement unit installed in the vehicle provides filtered signals of the vehicle’s responses to the longitudinal road profile. A disturbance observer was designed to extract the characteristic features of the road profile from the signals measured by the inertial measurement unit. Design synthesis based on a Kalman filter was used for the observer design. A nonlinear damper is explicitly considered to improve the estimation accuracy. Virtual measurement signals are introduced for observability. The suggested methodology estimates the road profile that is sufficiently accurate for localization. Based on the estimated longitudinal road profile, we generated spectrogram plots as the features for localization. The localization is realized by matching the spectrogram plot with pre-indexed plots. The localization using the estimated road profile shows a few meters accuracy, suggesting a possible road profile estimation method as an alternative sensor for vehicle localization.

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GPS signals are not reliable in urban canyons or inside tunnels. In those situations, accurate local positioning is possible through local landmarks. This paper presents the potential accuracy of such a landmark-based local positioning system and the desired attributes of landmarks influencing positioning accuracy. The analysis of the achievable accuracy and the sensitivity of the factors for precise vehicle positioning is performed using LiDAR sensor measurements in a controlled environment and a generic positioning method. The landmark-based positioning can achieve better than 0.2 m accuracy. The diameter and geometric configuration of the landmarks are the most important factors for higher accuracy. The results presented can guide the design and construction of a local positioning system in urban areas.

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