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This paper proposes an auto-decision making system, knowledge-based major choosing decision (KMCD), which helps remote students choose their majors. It is built within a first-order reasoning system with an uncertain reasoning function. The knowledge of both students who want to choose their majors and who have finished their studies are used in the decision making process of the system. KMCD provides personalized suggestions to those students who want to choose majors. Both the learning abilities of students and the career prospects of majors are carefully analyzed on the basis of the knowledge in database. As these analyzing results are well utilized in the decision making process of KMCD, it can be expected that the students who take the suggestions will have a better chance to get graduated smoothly and start their careers successfully after graduation. A detailed description of KMCD is given, which shows that the system is actually a reasonable decision making framework with a capability of expansion of other knowledge which is supposed to be useful for major choosing. An experimental study on data of real college students is devised to justify the effectiveness of KMCD. [ABSTRACT FROM PUBLISHER]

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment. [ABSTRACT FROM AUTHOR]