Abstract. We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.

The malarial parasite Plasmodium vivax causes disease in humans, including chronic infections and recurrent relapses, but the course of infection is rarely fatal, unlike that caused by Plasmodium falciparum. To investigate differences in pathogenicity between P. vivax and P. falciparum, we have compared the subtelomeric domains in the DNA of these parasites. In P. falciparum, subtelomeric domains are conserved and contain ordered arrays of members of multigene families, such as var, rif and stevor, encoding virulence determinants of cytoadhesion and antigenic variation. Here we identify, through the analysis of a continuous 155,711-base-pair sequence of a P. vivax chromosome end, a multigene family called vir, which is specific to P. vivax. The vir genes are present at about 600-1,000 copies per haploid genome and encode proteins that are immunovariant in natural infections, indicating that they may have a functional role in establishing chronic infection through antigenic variation.

Abstract. In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity/acceleration/turn). These are applied to four typical pedestrian motion types (crossing, stopping, bending in, starting). Position measurements are provided by an external state-of-the-art stereo vision-based pedestrian detector. We investigate the accuracy of position estimation and path prediction, and the benefit of the IMMs vs. the simpler single dynamical models. Special care is given to the proper sensor modeling and parameter optimization. The dataset and evaluation framework are made public to facilitate benchmarking.