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Road friction estimation algorithms had been studied for many years because it is very important factor for safety control and fuel efficiency of vehicle. But traditional solutions are hard to adapt in automotive industry because their performance is not sufficient enough and expensive to implement. Therefore, this paper proposes a road friction estimation algorithm based on a trained artificial neural-network which is low cost and robust. The suggested method doesn’t need expensive additional sensors such as optical or lidar sensor, also it shows better performance in real car environment compared to other algorithms based on vehicle dynamics. In this paper, we would describe this algorithm in detail and analyze the test results evaluated in real road conditions.

In this paper, switchable Lane Keeping System (LKS) and Active Lane Keeping Assist System (ALKAS) with early/late intervention criteria is proposed and developed. These two systems are commonly based on single track vehicle model and weighted lateral deviation prediction. The main difference is intervention strategy between two systems. Software In the Loop (SIL), Man In the Loop (MIL) verification are fulfilled for both systems till vehicle speed 200kph. For real vehicle verification, only LKS results shall be shown in this paper which shows small maximum lateral deviation also in road transition and vehicle speed variation. Real vehicle verification results for ALKAS shall not be shown because it is more related to steering feeling of driver.