Fredrik Sandblom

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We present a method for estimating mean and covariance of a transformed Gaussian random variable. The method is based on evaluations of the transforming function and resembles the unscented transform and Gauss-Hermite integration in that respect. The information provided by the evaluations is used in a Bayesian framework to form a posterior description of(More)
This paper presents a fusion architecture designed for vehicle manufacturers that use multiple sensor systems to realize several active safety applications, ranging from standard systems to autonomous systems. Advantages and disadvantages of design choices are discussed and the methods we have chosen to implement in two demonstrator vehicles are used as(More)
This paper is concerned with the use of Gaussian process regression based quadrature rules in the context of sigma-point-based nonlinear Kalman filtering and smoothing. We show how Gaussian process (i.e., Bayesian or Bayes-Hermite) quadratures can be used for numerical solving of the Gaussian integrals arising in the filters and smoothers. An interesting(More)
Reliable and accurate vehicle motion models are of vital importance for automotive active safety systems for a number of reasons. First of all, these models are necessary in tracking algorithms that provide the safety system with information. Second, the motion model is often used by the safety application to make long-term predictions about the future(More)
This paper is concerned with the problem of decision-making in systems that assist drivers in avoiding collisions. An important aspect of these systems is not only assisting the driver when needed but also not disturbing the driver with unnecessary interventions. Aimed at improving both of these properties, a probabilistic framework is presented for jointly(More)
This paper presents a probabilistic framework for decision-making in collision avoidance systems, targeting all types of collision scenarios with all types of single road users and objects. Decisions on when and how to assist the driver are made by taking a Bayesian approach to estimate how a collision can be avoided by an autonomous brake intervention, and(More)
In this paper we present a method for estimating mean and covariance of a transformed Gaussian random variable. The method is based on evaluations of the transforming function and resembles the unscented transform or Gauss-Hermite integration in that aspect. However, the information provided by the evaluations is used in a Bayesian framework to form a(More)
This paper addresses a practical issue that arises when the output from two local trackers, using two different state-space models, are to be fused with each other in a global track-to-track fusion system. Such fusion algorithms require the local tracks to be represented in a joint state-space, meaning that at least one of these tracks needs to be(More)
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