The previous knowledge of road environments is vital for driver assistance systems, and an accurate map, which provides information on the roads to be traveled, is desired. Accurate road information map preparation is still costly because a highly accurate positioning system and manual mapmaking operations are required. This paper proposes a method for automatic high accuracy map generation at low cost, using a standard in-vehicle camera and a standard GPS. The positioning results from GPS have relatively large error, so that the accuracy of a map based on these positioning results is not adequate. Our proposed method enables accurate map generation of a local area by a vehicle, and improves global accuracy by optimal integration of many local maps. This paper describes accurate local map generation based on precise trajectories derived from GPS Doppler, and evaluates the effectiveness of this method using actual data.

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Over the years, multiple engine manufacturers have offered mechanical turbo compounding on some engine models as a means to improve the efficiency of diesel engines. In theory, converting exhaust energy which would otherwise be wasted to shaft work seems like a technical path that no manufacturer should ignore especially in today’s world with the uncertainty of future CO2 emission regulations or fuel economy standards. This paper offers an independent examination of the effectiveness of the most recent implementation of mechanical turbo compounding on the Detroit Diesel (Daimler) DD15. In order to evaluate the effectiveness, an analysis procedure was developed for computing the tradeoff between power generated by the power turbine and losses incurred due to higher exhaust back-pressure as a result of the power turbine with consideration to the requirement for some minimum exhaust pressure to drive EGR.

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This paper proposes a technique of position estimation which is applicable to urban environments where the accuracy of GPS positioning is deteriorating. The method, utilizing GPS Doppler processing and gyros, calculates trajectories by extracting the most reliable azimuth, which is then integrated with vehicle speed. Our approach focuses on integrating these segments of the trajectory with GPS pseudoranges which are received at points throughout the whole trajectory. An evaluation test showed that our proposed method achieves more accurate position estimation than conventional methods, demonstrating its effectiveness.

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