This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.
This is a postprint version of the following published document: Martín, D., et al. (2014) a b s t r a c tThis paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Com putational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting edge applications to cope with perception difficulties, data processing algorithms, expert knowl edge, and decision making. The long term in vehicle applications, that are presented in this paper, out perform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and oth ers vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning sys tems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.
This paper presents a novel approach for learning self-awareness models for autonomous vehicles. Proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator. It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multimodal concepts. In the presented case, visual perception and localization are used as modalities. Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level. Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.
This paper evaluates the implementation of an adaptive technique for direct-state Kalman-filter (DSKF)-based scalar tracking loops used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In timevarying scenarios, the measurements' distribution changes over time due to noise, signal dynamics, multipath, and non-line-ofsight effects. In this kind of scenarios, it is not easy to find a suitable model, and the DSKF tends to be a suboptimal solution. This study introduces a method to adapt the noise covariances of the DSKF by using the loop-bandwidth control algorithm (LBCA). The LBCA adapts the loop bandwidth of the DSKF based on the statistics of the tracking channel. The presented technique is compared with the Cramér-Rao bound (CRB)based DSKF, which adjusts the measurement noise covariance depending on the CRB. These two adaptive DSKFs are compared with the LBCA-based standard scalar tracking loop (STL). The LBCA-based DSKF, the CRB-based DSKF, and the LBCA-based standard STL are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver is evaluated in simulated scenarios with different dynamics and noise cases. The results confirm that the LBCA-based DSKF exhibits superior dynamic tracking performance than the CRBbased DSKF. Moreover, the LBCA-based standard STL still shows the best dynamic tracking performance, while having the lowest complexity.Index Terms-Global navigation satellite system (GNSS), adaptive scalar tracking loop (A-STL), Kalman filtering (KF), directstate Kalman-filter (DSKF), loop-bandwidth control algorithm (LBCA).
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