The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.
@cite_22 propose an environmental model derived from a knowledge base with hierarchical classes and relations between the entities. The ontology is implemented in C++ to provide an environmental model, which was updated by sensor data and used to make online decisions in the test vehicle of the project Stadtpilot @cite_21 .
@cite_21 @cite_22
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Occupancy grids have shown interesting properties to model the environment for intelligent vehicles perception. In this paper, we present a novel approach to build 2D occupancy grid maps with stereo-vision. Our approach proposes a fitted sensor model based on the disparity space to interpret the stereo-vision information onto an occupancy grid map. The evidential model deals with sensor uncertainties by using Dempster-Shafer theory. Our approach exploits the U-disparity space to model the obstacle information and the V-disparity space to model the road space information. The fusion of these two sources of complementary information results to an enhanced environmental model. In a first experimental data set, results based on real road data and comparisons with Lidar grids show that the proposed evidential sensor model can model efficiently the environment. In a second one, the mapping of a road environment is reported to show the performance of the proposed model with another stereo-vision system. This paper deals with the integration of agent technology in the emerging field of vehicular networks. The agents are empowered with domain knowledge and they can perform geospatial and temporal reasoning. For the domain knowledge we developed a vehicular network ontology. The geospatial reasoning is performed with AllegroGraph, while event reasoning in RacerPro. The vehicle overtaking scenario is used to exemplify our solution. We show how a robot can autonomously learn an ontology of objects to explain aspects of its sensor input from an unknown dynamic world. Unsupervised learning about objects is an important conceptual step in developmental learning, whereby the agent clusters observations across space and time to construct stable perceptual representations of objects. Our proposed unsupervised learning method uses the properties of allocentric occupancy grids to classify individual sensor readings as static or dynamic. Dynamic readings are clustered and the clusters are tracked over time to identify objects, separating them both from the background of the environment and from the noise of unexplainable sensor readings. Once trackable clusters of sensor readings (i.e., objects) have been identified, we build shape models where they are stable and consistent properties of these objects. However, the representation can tolerate, represent, and track amorphous objects as well as those that have well-defined shape. In the end, the learned ontology makes it possible for the robot to describe a cluttered dynamic world with symbolic object descriptions along with a static environment model, both models grounded in sensory experience, and learned without external supervision. Received 28 May 2002; revised 18 September 2002; accepted 24 September 2002; published 14 February 2003. [1] When an inverse problem is solved to estimate an unknown function such as the hydraulic conductivity in an aquifer or the contamination history at a site, one constraint is that the unknown function is known to be everywhere nonnegative. In this work, we develop a statistically rigorous method for enforcing function nonnegativity in Bayesian inverse problems. The proposed method behaves similarly to a Gaussian process with a linear variogram (i.e., unrestricted Brownian motion) for parameter values significantly greater than zero. The method uses the method of images to define a prior probability density function based on reflected Brownian motion that implicitly enforces nonnegativity. This work focuses on problems where the unknown is a function of a single variable (e.g., time). A Markov chain Monte Carlo (MCMC) method, specifically, a highly efficient Gibbs sampler, is implemented to generate conditional realizations of the unknown function. The new method is applied to the estimation of the trichloroethylene (TCE) and perchloroethylene (PCE) contamination history in an aquifer at Dover Air Force Base, Delaware, based on concentration profiles obtained from an underlying aquitard. INDEX TERMS: 1831 Hydrology: Groundwater quality; 1869 Hydrology: Stochastic processes; 3260 Mathematical Geophysics: Inverse theory; KEYWORDS: stochastic inverse modeling, contaminant source identification, inference under constraints, Markov chain Monte Carlo (MCMC), Gibbs sampling, Bayesian inference