12,028 research outputs found
Real-time Motion Planning For Autonomous Car in Multiple Situations Under Simulated Urban Environment
Advanced autonomous cars have revolutionary meaning for the automobile industry. While more and more companies have already started to build their own autonomous cars, no one has yet brought a practical autonomous car into the market. One key problem of their cars is lacking a reliable active real-time motion planning system for the urban environment. A real-time motion planning system makes cars can safely and stably drive under the urban environment. The final goal for this project is to design and implement a reliable real-time motion planning system to reduce accident rates in autonomous cars instead of human drivers. The real-time motion planning system includes lane-keeping, obstacle avoidance, moving car avoidance, adaptive cruise control, and accident avoidance function. In the research, EGO vehicles will be built and equipped with an image processing unit, a LIDAR, and two ultrasonic sensors to detect the environment. These environment data make it possible to implement a full control program in the real-time motion planning system. The control program will be implemented and tested in a scaled-down EGO vehicle with a scaled-down urban environment. The project has been divided into three phases: build EGO vehicles, implement the control program of the real-time motion planning system, and improve the control program by testing under the scale-down urban environment. In the first phase, each EGO vehicle will be built by an EGO vehicle chassis kit, a Raspberry Pi, a LIDAR, two ultrasonic sensors, a battery, and a power board. In the second phase, the control program of the real-time motion planning system will be implemented under the lane-keeping program in Raspberry Pi. Python is the programming language that will be used to implement the program. Lane-keeping, obstacle avoidance, moving car avoidance, adaptive cruise control functions will be built in this control program. In the last phase, testing and improvement works will be finished. Reliability tests will be designed and fulfilled. The more data grab from tests, the more stability of the real-time motion planning system can be implemented. Finally, one reliable motion planning system will be built, which will be used in normal scale EGO vehicles to reduce accident rates significantly under the urban environment.No embargoAcademic Major: Electrical and Computer Engineerin
A sufficient condition for a finite-time singularity of the 3d Euler Equations
A sufficient condition is derived for a finite-time singularity of the 3d incompressible Euler equations, making appropriate assumptions on eigenvalues of the Hessian of pressure. Under this condition , where moves with the fluid. In particular, , , and all become unbounded at one point , being the first blow-up time in
Toward high-fidelity coherent electron spin transport in a GaAs double quantum dot
In this paper, we investigate how to achieve high-fidelity electron spin
transport in a GaAs double quantum dot. Our study examines spin transport from
multiple perspectives. We first study how a double dot potential may
affect/accelerate spin relaxation. We calculate spin relaxation rate in a wide
range of experimental parameters and focus on the occurrence of spin hot spots.
A safe parameter regime is identified in order to avoid these spin hot spots.
We also study the non-adiabatic transitions in the Landau-Zener process of
sweeping the interdot detuning, and propose a scheme to take advantage of
possible Landau-Zener-St\"{u}kelburg interference to achieve high-fidelity spin
transport at a higher speed. Finally, we calculate the double-dot correction on
the effective -factor for the tunneling electron, and estimate the resulting
phase error between different spin states. Our results should provide a useful
guidance for future experiments on coherent electron spin transport.Comment: 10 pages, 7 figure
Focused information criterion and model averaging for generalized additive partial linear models
We study model selection and model averaging in generalized additive partial
linear models (GAPLMs). Polynomial spline is used to approximate nonparametric
functions. The corresponding estimators of the linear parameters are shown to
be asymptotically normal. We then develop a focused information criterion (FIC)
and a frequentist model average (FMA) estimator on the basis of the
quasi-likelihood principle and examine theoretical properties of the FIC and
FMA. The major advantages of the proposed procedures over the existing ones are
their computational expediency and theoretical reliability. Simulation
experiments have provided evidence of the superiority of the proposed
procedures. The approach is further applied to a real-world data example.Comment: Published in at http://dx.doi.org/10.1214/10-AOS832 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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