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In recent decades, research and development in the field of autonomous vehicles have rapidly increased throughout the world, and autonomous driving technologies have begun to be applied to mass production vehicles. Especially recently, even affordable mass production vehicles have begun to be equipped with some autonomous driving systems such as a Lane Keeping Assist (LKA) system. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage or blurred lane markings. To overcome this limitation, we have developed Global Navigation Satellite System (GNSS)-based LKA systems, which are not affected by the surrounding environment such as weather and the condition of lane markings.

Recently, the field of autonomous driving has been dramatically expanding, and some of the key technologies like the Lane Keeping Assist (LKA) system have begun to be applied to mass production vehicles. In general, mass-produced LKA systems use a lane detection camera as a means of keeping the lane. One of the common limitations of camera-based LKA systems is that the lane keeping performance significantly decreases when the camera cannot detect lane markings for various reasons such as snow coverage and sunlight. To overcome this limitation, we have developed a Global Navigation Satellite System (GNSS) based LKA system, which is not affected by the surrounding environment such as weather and lighting. Our LKA system uses centimeter-level augmentation service and high-definition maps, whereby the LKA system can accurately estimate its own position.

Accuracy of positioning with GNSS (Global Navigation Satellite System) has been improved in recent years. Especially in Japan, high accuracy GNSS service, QZSS (Quasi Zenith Satellite System), will start in 2018 and the first QZS, “MICHIBIKI” has been already launched. They will broadcast correction data which enhances GNSS performance and realize cm-order positioning. In this paper, we, Mitsubishi Electric develop the estimation algorithm of vehicle position and attitude and also adapt the algorithm to a test vehicle which can trace automatically the calculated path with EPS (Electric Power Steering) and high accuracy GNSS. Although the GNSS receiver calculates the longitude and latitude of the vehicle every second, it is not enough to control vehicle dynamics smoothly. So we estimate vehicle position and attitude of the vehicle with GNSS and vehicle sensors in high frequency.

In this paper, we suggest a novel algorithm to distinguish semi-steady states from various steering patterns and to estimate the stability factor. The algorithm also estimates each stability factor in left and right turns because there could be a case where they differ based on uneven tire wear and so on. The stability factor, which is the turning characteristic of a vehicle, has been treated as constant for most vehicle control systems. However, in fact, it may change in some situations, for example when a vehicle is overloaded. So there is a chance that a driver may be aware of an unusual sensation when vehicle control is designed based on a constant stability factor. We have succeeded in developing an algorithm to estimate the stability factor accurately enough to be able to compensate for it and have confirmed the effectiveness of the algorithm by simulation and vehicle testing as well.