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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This paper proposes an inertial sensor-based positioning method without using dead reckoning. The basic concept estimates the current position based on a pre-indexed map composed of spectrograms of road shapes estimated using inertial sensor signals. The proposed positioning algorithm has three characteristics: a feature-indexed map, the Kalman filter for signal fusion, and the interacting multiple model to integrate multiple maps. The positioning algorithm shows higher positioning accuracy and shorter computing time than a simple scanning method. Furthermore, the size of the map does not proportionally increase as the total length of the road increases. Therefore, the proposed positioning algorithm can be used as a complementary or alternative positioning method to GPS-based positioning methods using only an inertial sensor that is cheap and reliable.

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Measuring road profile is mainly used for a road maintenance purpose. Another interesting application of the knowledge on road profile is vehicle localization if the knowledge is available in real-time. A cost effective and implementable approach of measuring or estimating a road profile in a passenger vehicle is estimating the profile using inertial sensors that is readily installed for active safety features. The suggested method is a Kalman filter based disturbance observer without assuming a constant disturbance. By estimating the disturbance of k-1 time step at the k time step, the constant disturbance assumption is not necessary. The required observer structure and dynamics are presented as well as simulation and experimental results.