It is important that automotive engineers understand the interactions between active human maneuvering motions and vehicle dynamics, and how vehicle control affects the physical sensations of the human driver. This paper proposes a new system framework, the human model-based active driving system (HuMADS) for simulating human driver-vehicle interactions. HuMADS integrates the vehicle controller with models of vehicle dynamics and human biomechanics. It has an hierarchical closed-loop architecture for driver-vehicle control systems, including structures and contact interfaces of human and vehicle bodies. HuMADS is based on the OpenSim simulation platform. The developed system regulates the human model dynamics, such that the human model can react realistically to vehicle maneuver motions. The usability of the HuMADS is demonstrated through the simulation of coordinated gas/brake pedal operation and wheel-steering in highway driving tasks. The simulated vehicle dynamics and vehicle maneuvers are comparable with previously published experimental data of car-following driving. In addition, the proposed controllers successfully maintain the human body's balance inside the vehicle during vehicle maneuvers. We are convinced that the HuMADS has potential as a tool for the development of intelligent transportation systems and investigation of integrated safety.

This work presents a novel method to efficiently factorize the combination of multiple factor graphs having common variables of estimation. Variable ordering, a well-known variable elimination technique in linear algebra is employed to efficiently solve a factor graph. Our primary contribution in this work is to reuse the variable ordering of the graphs being combined to find the ordering of the fused graph called fusion ordering. By reusing the variable ordering of the parent graphs we were able to produce an order-of-magnitude difference in the time required for solving the fused graph. A formal verification is provided to show that the proposed strategy does not violate any of the relevant standards. The fusion ordering is experimented on the standard dataset used in the sparse linear algebra community called SuiteSparse [1]. Recent factor graph formulation for Simultaneous Localization and Mapping (SLAM) like Incremental Smoothing and Mapping (ISAM) using the Bayes tree has been very successful and garnered much attention. In the case of mapping, multi-robot system has a great advantage over a single robot that provides faster map coverage and better estimation quality. We also demonstrate the improvement of our ordering scheme on a real-world multi-robot AP Hill dataset [2].

High-fidelity computational human models provide a safe and cost-efficient method for studying driver experience in vehicle maneuvers and for validation of vehicle design. Compared to passive human models, active human models capable of reproducing the decision-making, as well as vehicle maneuver motion planning and control, will be able to support realistic simulation of human-vehicle interaction. In this paper, we propose an integrated human-vehicle interaction simulation framework which learns vehicle maneuver motion primitives from human drivers, and uses them to compose natural and contextual driving motions. Specifically, we recruited six experienced drivers and recorded their vehicle maneuver motions on a fixed-base driving simulation testbed. We further segmented and classified the collected data based on their similarity in joint coordination. Using a combination of imitation learning methods, we extracted the regularity and variability of vehicle maneuver motions across subjects, and learned the dynamic motion primitives to be used for motion reproduction in simulation. We present an implementation of the framework on lower-extremity joint coordination in pedal activation for longitudinal vehicle control. Our research efforts lead to a motion primitive library which enables planning natural driver motions, and will be integrated with the driving decision-making, motion control, and vehicle dynamics in the proposed framework for simulating human-vehicle interaction.

Due to the extended imaging times employed in single photon emission computed tomography (SPECT) and positron emission tomography (PET), patient motion during imaging is a common clinical occurrence. The fast and accurate correction of the three-dimensional (3-D) translational and rotational patient motion in iterative reconstruction is thus necessary to address this important cause of artifacts. We propose a method of incorporating 3-D Gaussian interpolation in the projector/backprojector pair to facilitate compensation for rigid-body motion in addition to attenuation and distance-dependent blurring. The method works as the interpolation step for moving the current emission voxel estimates and attenuation maps in the global coordinate system to the new patient location in the rotating coordinate system when calculating the expected projection. It also is employed for moving back the backprojection of the ratio of the measured projection to the expected projection and backprojection of the unit value (sensitivity factor) to the original location. MCAT simulations with known six-degree-of-freedom (6DOF) motion were employed to evaluate the accuracy of our method of motion compensation. We also tested the method with acquisitions of the data spectrum anthropomorphic phantom where motion during SPECT acquisition was measured using the Polaris IR motion tracking system. No motion artifacts were seen on the reconstructions with the motion compensation.

A problem in teaching large introductory computer science courses is to overcome the impersonality of the large lecture class and to provide more personal attention to individual students.Our approach as to use peer [earning experiences to instill in students the need to take responsibility for their learning and for ihe learning of those around them.Recent work has shown that educational quality for students and productivity for faculty can be enhanced through use of peer-learning environments where students do not just learn and faculty do not just teach.The novel aspects of our work are to apply group learning in a large introductory computer science class setting and to expect more responsibility on the part of students for their learning.In support of these goals we have introduced the use of upper-level undergraduate students to help facilitate student group interaction.In addition, we have developed software to minimize the administrative overhead of handling many groups and for students to electronically record group learning activities.

Multiscale relaxation labeling for segmentation of fractal images is described. The images used are of pavement distress, on which simple edge detection schemes perform poorly. Relaxation labeling is used to improve upon initial edge-based segmentation. A multiscale relaxation technique is used in a pavement distress detection system. To better model pixel interactions, nonlinear terms are included in the relaxation process. Symmetry arguments and careful engineering allow a 93% reduction in the complexity of this approach. To demonstrate the necessity of the multiscale approach, examples with and without multiscale relaxation are shown. It is found that performance is greatly improved by multiscale relaxation.>

The boundary element method (BEM) for solving Poisson's equations is described. Issues in BEM, such as Green's functions, boundary conditions, evaluation of improper integrals, and continuity up to first derivative of solution functions, are discussed. BEM is compared with FEM, the finite element method, in terms of storage and time complexity. The authors discuss application to vision: height from gradient; shape from shading; surface interpolation; brightness based stereo matching; and the optical flow problem. Brief mention is made of some early experimental results on synthetic images.>

This paper presents a stereo matching method, based on image brightness matching, that does not rely on correlatitsn. We develop a model for how brightness changes from one irnage to another in a stereo pair. A spatially-varying multiplier is used to relate image brightnesses, so that matching of dissimilx greyvalues can occur. The problem of stereo matching then depends upon solving for the multiplier and disparity fields. The so!ution is obtained by formulating an equivalent minimization problem, then solving the minimization problem using the variational calculus. We describe the implementation on a highly parallel computer, and present sample results.

An important problem in computer vision is the determination of weights for multiple objective function optimization. This problem arises naturally in many reconstruction problems, where one wishes to reconstruct a function belonging to a constrained class of signals based upon noisy observed data. A common approach is to combine the objective functions into a single total cost function. The problem then is to determine appropriate weights for the objective functions. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min{Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. 1