Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning

Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works have been studied in the decision-making and motion planning module. However, the reliability and the stability of the neural network is still full of uncertainty. In this paper, we introduce a hierarchical planning architecture including a high-level grid-based behavior planner and a low-level trajectory planner, which is highly interpretable and controllable. As the high-level planner is responsible for finding a consistent route, the low-level planner generates a feasible trajectory. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.Comment: 6 pages, 8 figures, accepted by IROS202

Manipulating atomic defects in plasmonic vanadium dioxide for superior solar and thermal management

Vanadium dioxide (VO2) is a unique active plasmonic material due to its intrinsic metal-insulator transition, remaining less explored. Herein, we pioneer the method to tailor the VO2 surface plasmon by manipulating its atomic defects and establish a universal quantitative understanding based on seven representative defective VO2 systems. The record high tunability is achieved for the localized surface plasmon resonance (LSPR) energy (0.66–1.16 eV) and transition temperature range (40-100 oC). Drude model and density function theory reveal the charge of cations plays a dominant role over the numbers of valence electrons to determine the free electron concentration. We further demonstrate their superior performances in extensive unconventional plasmonic applications including energy-saving smart windows, wearable camouflage devices, and encryption inks.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionY. Long is thankful for the funding support from the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, Sino-Singapore International Joint Research Institute, and Minister of Education Singapore Tier 1 RG86/20 and RG103/19 for funding support. Z. M. Sun is thankful for the funding support from National Key Research and Development Program of China (Grant No. 2017YFB0701700). Y. Zhong, Y. Liu, and X. Ye were supported by the Indiana University FRSP Grant and IU-MSI STEM Initiative Seed Grant

Remotely monitoring offshore wind turbines via ZigBee networks embedded with an advanced routing strategy

As better wind speeds are available offshore compared to on land, offshore wind power contribution in terms of electricity supplied is higher, thus more and more offshore wind turbines have been and will be deployed. However, the severe offshore conditions make it necessary to develop reliable and cost-effective real-time monitoring system when building offshore wind power farms. This paper proposes an innovative method for designing remote monitoring system for offshore wind turbines based on ZigBee wireless sensor networks. ZigBee networks carrying variety of sensors actively collect dynamic data related to the system operation status, including parameters of the mechanical unit and electrical unit as well as the operation environment. Each wind turbine itself represents a single wireless network, which sends information to remote monitoring center by GPRS module to achieve full wireless communication. To enhance the topologic efficiency and reduce the energy consumption of the networks, an optimized routing algorithm is developed. A physical system based on such method is developed. Analysis and experiment tests with real wind farm data indicate that the developed system works fairly well. The fundamental idea as studied in this work is of great value for building reliable and affordable real-time monitoring systems for wind farms (offshore and on land) with enhanced safety and efficiency