This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving characteristic estimator and a driving behavior predictor. A driver's implicit driving characteristic information is uniquely determined and detected by proposed the online-estimator. Neural-network based behavior predictor is developed and validated by testing with the real naturalistic traffic data from Next Generation Simulation (NGSIM), which demonstrates the effectiveness in identifying the driving characteristics and transforming into accurate behavior prediction in real-world traffic situations.
In view of the distribution network operation problems caused by many distributed generations integration to distribution network, and the increasingly serious peak valley imbalance in grid, this paper proposes a coordinated control system of source-network-load-storage based on virtual power plant technology, which is designed and implemented in the actual project. The system uses cloud platform technology and multi-energy complementary technology to realize coordination and optimization control mechanism between sources, network and loads in regional distribution network. The system is based on the distribution Internet of things cloud master platform. Through the virtual power plant technology, resources such as cogeneration, photovoltaic, wind, distributed energy storage, electric vehicles, flexible loads are aggregated to achieve coordinated and unified control, realize the optimal operation of multi-energy complementary. It can accept the control of the dispatching center, and participate in the power transaction of demand response. Based on the application scenario, this paper explains how to use virtual power plant technology to participate in demand response power transaction, and describes the transaction rules and processes. The system is beneficial to the safety and reliability of the distribution network. It can implement reasonable configuration and consumption of distribution generations. It can control controllable loads such as electric vehicle charging pile to participate in peak regulation of power grid, and provide strong technical support for the realization of demand response.
Abstract-Among the different types of robots, modular and self-reconfigurable robots such as SuperBot have less limitations than their counterparts due to their versatility of gaits and increased dynamic adaptability. This results in a highly dexterous and adjustable robot suitable for many environments. This however, usually comes at the expense of a necessary human observer required to monitor and control the robot manually resulting in a waste of power and time. Thus, an intelligent system would be indispensable in optimzing the behavior and control of modular and self-reconfigurable robots. This paper presents an Intelligent Online Reconfiguration System (IORS) which through a combination of learning and reasoning, increases the efficiency in control and movement of the modular and self-reconfigurable robot called Superbot. Using this system, Superbot is able to learn and choose the best gait automatically by sensing its current environment (e.g., friction or slope). As a result, the IORS implementation in SuperBot achieves: 1) correct slope gradient sensing, 2) best gait learning to traverse different slopes, and 3) rational decision making for choosing the best gait.
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