Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

To assure that an autonomous car is driving safely on public roads, its object detection module should not only work correctly, but show its prediction confidence as well. Previous object detectors driven by deep learning do not explicitly model uncertainties in the neural network. We tackle with this problem by presenting practical methods to capture uncertainties in a 3D vehicle detector for Lidar point clouds. The proposed probabilistic detector represents reliable epistemic uncertainty and aleatoric uncertainty in classification and localization tasks. Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion. The results also show that we can improve the detection performance by 1%-5% by modeling the aleatoric uncertainty.Comment: Accepted to present in the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018

Volume Modeling for Rapid Prototyping

The expanding workspace of Rapid Prototyping will draw on the new developments in geometric modeling. Volume modeling has substantial advantages over other modeling schemes to meet the emerging requirements of Rapid Prototyping technology. It provides us with a new approach to design complex geometry and topology. The integration of the volume modeling and Rapid Prototyping technology will help us to fully exploit RP's ability to fabricate objects with complex structures. This paper addresses our research and practice in a volume modeling system toward Rapid Prototyping. Novel techniques in volumetric data manipulation, NURBS volume models and triangular facet generation over solid models are presented. Computer models designed by this system and their corresponding DTM products are also shown atthe end of this paper.Mechanical Engineerin

Transverse emission of isospin ratios as a probe of high-density symmetry energy in isotopic nuclear reactions

Transverse emission of preequilibrium nucleons, light clusters (complex particles) and charged pions from the isotopic $^{112,124}$Sn+$^{112,124}$Sn reactions at a beam energy of 400\emph{A} MeV, to extract the high-density behavior of nuclear symmetry energy, are investigated within an isospin and momentum dependent transport model. Specifically, the double ratios of neutron/proton, triton/helium-3 and $\pi^{-}/\pi^{+}$ in the squeeze-out domain are analyzed systematically, which have the advantage of reducing the influence of the Coulomb force and less systematic errors. It is found that the transverse momentum distribution of isospin ratios strongly depend on the stiffness of nuclear symmetry energy, which would be a nice observable to extract the high-density symmetry energy. The collision centrality and the mass splitting of neutron and proton in nuclear medium play a significant role on the distribution structure of the ratios, but does not change the influence of symmetry energy on the spectrum.Comment: 5 figures, 13 page