In this talk John Ousterhout, Professor, Computer Science, will explain why device swarms of the future are likely be controlled using large-scale software systems running in datacenters, and describe some of the projects underway in the Stanford Platform Laboratory to create infrastructure for these control systems.
High energy density and long-lasting lithium-ion batteries are key toward the next-generation electric vehicles. The main challenges facing materials engineer today are the long development time for components such as positive and negative electrodes, and the rare events that lead to degradations. In this talk, William Chueh, Assistant Professor, Materials Science and Engineering will present new tools developed at Stanford to accelerate mechanistic understanding of lithium-ion batteries.
How can combustion research potentially help in future hybrid ground-vehicle power systesm? Prof. Hai Wang, Mechanical Engineering, presents a brief review of current state of knowledge about real fuel combustion followed by a discussion of ongoing fundamental studies of miniaturized detonation with potential application in micro-turbine based electricity generation.
Prof. Juan Rivas-Davila, assistant professor, electrical engineering, will explain the research directions of his group as well as some of the challenges faced in designing converters in the HF and VHF frequency range. He will also discuss some of his lab’s recent findings regarding the performance of Wide Bandgap power devices when operating at frequencies above 10MHz, and some preliminary results of their work on wireless power transfer in automotive applications.
Shiwen Zhang, with the Stanford GPS Lab discusses several multipath detection and mitigation techniques both traditional and newly proposed. In recent years, due to self-driving car research and the emerging interest in the development of high definition maps, multipath detection methods using 3D maps and ray tracing algorithms are being heavily studied.
With the emergence of autonomous vehicles, it is important to understand how their behaviors may impact the transportation system. In this talk, we will introduce an analytical traffic model for unmanaged intersections considering various vehicle behaviors. The steady state traffic properties (e.g. delay) under various vehicle behaviors are studied. The accuracy of the model is verified against traffic simulation.
The Center for Interdisciplinary Brain Sciences Research (CIBSR) in a collaboration with the Dynamic Design Lab (DDL), are using fNIRS to study the patterns of brain activation that underlie driving behavior. In this webinar Lene Harbott, Stanford Research Scientist, will discuss recent findings on these subjects, and will describe research that relates personality traits to differences in driving strategy.
Deep neural networks are widely used for nonlinear function approximation with applications spanning from computer vision to control. Although these networks involve the composition of simple arithmetic operations, it can be very challenging to verify whether a particular network satisfies certain input-output properties. In this talk, Changliu Liu,
Former CARS Fellow, assistant professor at CMU will present methods that have emerged recently for soundly verifying such properties.
Martin Holder, Visiting Student Researcher in the Stanford Intelligent Systems Lab and Research Assoate and PhD candidate at the Institute of Automotive Engineering . TU Darmstadt, Germany discusses the role of perception sensor models within the toolchain for virtual testing of automated driving. He focuses on ongoing research on sensor modeling for radar and lidar sensors and structures open issues.
