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This study was conducted to investigate chemical properties of wild Lactuca indica (WL) and cultivated Lactuca indica (CL). The proximate composition, reducing sugar, free amino acids, organic acid, vitamin C, minerals, chlorophyll, and crude saponin were analyzed. WL and CL contained high levels of carbohydrate. The leaves and roots of CL contained higher levels of free amino acid than those of WL. Especially, the proline content of CL leaf was 12 times higher than that of WL leaf, and the arginine content of CL root was 100 times higher than that of WL root. The major organic acid and mineral of Lactuca indica were tartaric acid and potassium, respectively. CL showed significantly higher value of reducing sugar than WL. The vitamin C content of the samples ranged from 0.4 to 24.1 mg%, and CL leaf was the highest amount of vitamin C among the samples. CL leaf had a higher amount of chlorophyll than WL leaf, but WL root contained a higher amount of crude saponin than CL root. As in this study, CL showed better nutritional properties than WL, and these results will provide fundamental data in order to activate the cultivation of wild plants.

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This paper presents a model predictive control (MPC) approach to control the steering angle in an autonomous vehicle. In designing a highly automated driving control algorithm, one of the research issues is to cope with probable risky situations for enhancement of safety. While human drivers maneuver the vehicle, they determine the appropriate steering angle and acceleration based on the predictable trajectories of surrounding vehicles. Likewise, it is required that the automated driving control algorithm should determine the desired steering angle and acceleration with the consideration of not only the current states of surrounding vehicles but also their predictable behaviors. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, we define a safe driving envelope with the consideration of probable risky behaviors among the predicted probable behaviors of surrounding vehicles over a finite prediction horizon. For the control of the vehicle while satisfying the safe driving envelope and system constraints over a finite prediction horizon, a MPC approach is used in this research. At each time step, MPC based controller computes the desired steering angle to keep the subject vehicle in the safe driving envelope over a finite prediction horizon. Simulation and experimental tests show the effectiveness of the proposed algorithm.Keywords: automated driving vehicle, intelligent safety vehicle, model predictive control, Risk management system, automated steering control algorithm, safe driving envelope, probabilistic prediction