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This article proposes a new preceding vehicle detection framework for challenging lighting environments using a novel feature fusion technique based on an adaptive neuro-fuzzy inference system. A combination of two feature descriptors, the histogram of oriented gradients and local binary patterns, is adopted to improve the vehicle detection accuracy of the proposed framework, and the performance of the combination in image transformations is evaluated. Furthermore, we tested the detection performance of the proposed framework in three challenging driving conditions and filmed the test image sequences for each categorized environment of the experiments. The experimental results demonstrate that the proposed framework outperforms the conventional framework under specific driving environments with harsh lighting conditions.

This paper addresses a local environment recognition system for obstacle avoidance. In vision systems, obstacles that are located beyond the Field of View (FOV) cannot be detected precisely. To deal with the FOV problem, we propose a 3D Panoramic Environment Map (PEM) using a Modified SURF algorithm (MSURF). Moreover, in order to decide the avoidance direction and motion automatically, we also propose a Complexity Measure (CM) and Fuzzy‐Logic‐based Avoidance Motion Selector (FL‐AMS). The CM is utilized to decide an avoidance direction for obstacles. The avoidance motion is determined using FL‐AMS, which considers environmental conditions such as the size of obstacles and available space. The proposed system is applied to a humanoid robot built by the authors. The results of the experiment show that the proposed method can be effectively applied to a practical environment

-Recently, home energy management system (HEMS) for power consumption reduction has been widely used and studied. The HEMS performs electric power consumption control for the indoor electric device connected to the HEMS. However, a traditional HEMS is used for passive control method using some particular power saving devices. Disadvantages with this traditional HEMS is that these power saving devices should be newly installed to build HEMS environment instead of existing home appliances. Therefore, an HEMS, which performs with existing home appliances, is needed to prevent additional expenses due to the purchase of state-of-the-art devices. In this paper, an intelligent inference algorithm for EMS at home for non-power saving electronic equipment, called legacy devices, is proposed. The algorithm is based on the adaptive network fuzzy inference system (ANFIS) and has a subsystem that notifies retraining schedule to the ANFIS to increase the inference performance. This paper discusses the overview and the architecture of the system, especially in terms of the retraining schedule. In addition, the comparison results show that the proposed algorithm is more accurate than the classic ANFIS-based EMS system.