Probabilistic Threat Assessment and Driver Modeling in Collision Avoidance Systems

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 the probability that the driver will consider the intervention as motivated. The driver model makes it possible to initiate earlier braking when it is estimated that the driver acceptance for interventions is high. The framework and the proposed driver model are evaluated in several scenarios, using authentic tracker data and a differential GPS. It is shown that the driver model can increase the benefit of collision avoidance systems — particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict, e.g. when a playing child enters the roadway

Moment Estimation Using a Marginalized Transform

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 the parameters in a model of the transforming function. Estimates are then derived by marginalizing these parameters from the analytical expression of the mean and covariance. An estimation algorithm, based on the assumption that the transforming function can be described using Hermite polynomials, is presented and applied to the non-linear filtering problem. The resulting marginalized transform (MT) estimator is compared to the cubature rule, the unscented transform and the divided difference estimator. The evaluations show that the presented method performs better than these methods, more specifically in estimating the covariance matrix. Contrary to the unscented transform, the resulting approximation of the covariance matrix is guaranteed to be positive-semidefinite

Oxygen- and capacity-limited thermal tolerance: blurring ecology and physiology

No abstract available

Filtering and modelling for automotive safety systems

This thesis makes five important contributions to the development of an automotive safety system: filtering algorithms, three modelling frameworks concerning the usage of radar detections in tracking, vehicle motion, and decision-making for intervention decisions, and finally the implementation architecture.In the filtering context, we have developed a new sigma-point method for estimating the moments of a transformed Gaussian random variable. These estimates are derived from analytical expressions and are based on evaluations of the transforming function. The method is applied to the moment estimation task in a Gaussian filter and the resulting algorithm is denoted the marginalised Kalman filter (MKF).Compared to traditional radar models, ours is specifically designed for vehicle radars, which often yield several measurements from each object. These measurements can provide useful information, such as vehicle orientation, if they are accurately modelled. We introduce a tracking filter using such a sensor model, and show how the complex data association problem can be facilitated by merging similar hypotheses into groups.The presented vehicle motion model includes the control input from the driver. Uncertainties regarding, e.g., driver style, are formally treated with increased prediction accuracy as a result. Similar to this model, the third framework also takes the driver into consideration by allowing interventions only when the driver is believed to accept them. Our evaluations indicate an increased benefit in collision avoidance systems --- particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict.Finally, we present a modular functional design for implementing a real-time data fusion system. We conclude that a tracking system, using modern estimation techniques, is well suited for sensor data fusion in an automotive environment

Filtering and modelling for automotive safety systems

This thesis makes five important contributions to the development of an automotive safety system: filtering algorithms, three modelling frameworks concerning the usage of radar detections in tracking, vehicle motion, and decision-making for intervention decisions, and finally the implementation architecture.In the filtering context, we have developed a new sigma-point method for estimating the moments of a transformed Gaussian random variable. These estimates are derived from analytical expressions and are based on evaluations of the transforming function. The method is applied to the moment estimation task in a Gaussian filter and the resulting algorithm is denoted the marginalised Kalman filter (MKF).Compared to traditional radar models, ours is specifically designed for vehicle radars, which often yield several measurements from each object. These measurements can provide useful information, such as vehicle orientation, if they are accurately modelled. We introduce a tracking filter using such a sensor model, and show how the complex data association problem can be facilitated by merging similar hypotheses into groups.The presented vehicle motion model includes the control input from the driver. Uncertainties regarding, e.g., driver style, are formally treated with increased prediction accuracy as a result. Similar to this model, the third framework also takes the driver into consideration by allowing interventions only when the driver is believed to accept them. Our evaluations indicate an increased benefit in collision avoidance systems --- particularly in traffic situations where the future trajectory of another road user is hard for the driver to predict.Finally, we present a modular functional design for implementing a real-time data fusion system. We conclude that a tracking system, using modern estimation techniques, is well suited for sensor data fusion in an automotive environment

Marginalized sigma-point filtering

In this paper we present a method for estimatingmean and covariance of a transformed Gaussian random variable.The method is based on evaluations of the transformingfunction and resembles the unscented transform or Gauss–Hermite integration in that aspect. However, the informationprovided by the evaluations is used in a Bayesian frameworkto form a posterior description of the transforming function.Estimates are then derived by marginalizing the function fromthe analytical expression of the mean and covariance. An estimationalgorithm, based on the assumption that the transformingfunction is constructed by Hermite polynomials, is presented andcompared to the cubature rule and the unscented transform. Contraryto the unscented transform, the resulting approximation ofthe covariance matrix are guaranteed to be positive-semidefiniteand the algorithm performs much better than the cubature rulefor the evaluated scenario

Mandatory audit firm rotation : A study of the applicability of international experiences in a Swedish context

Senast den 17 juni 2016 ska Sverige ha implementerat regleringar kring obliga- torisk byrårotation i nationell rätt vilket då kommer kräva att noterade bolag och finansiella företag roterar revisionsbyrå i huvudregel var 10:e år. Målet från EU:s sida med att införa obligatorisk byrårotation i dess medlemsländer är att regleringen ska ha positiva effekter på revisorns oberoende, revisionskvali- teten och konkurrenssituationen på revisionsmarknaden. Syftet med vår studie är att ge ökad förståelse om hur revisionskvaliteten och revisorns oberoende påverkas av ett införande av obligatorisk byrårotation utifrån den forskning som finns internationellt. Eftersom studier kring obliga- torisk byrårotation ännu har kunnat genomföras i Sverige vill vi också bidra med kunskap om hur väl dessa internationella studier kan appliceras i ett svenskt sammanhang. Studien är baserad på inhämtning av sekundärdata där vi utgått från existe- rande forskning kring obligatorisk byrårotation. Val av vårt tillvägagångssätt kan motiveras med att en stor mängd forskning redan existerar och vi har velat samla in data som är så neutral som möjligt. Vår studie visar att resultaten från internationellt genomförda studier tyder på att revisionskvaliteten sjunker vid ett införande av obligatorisk byrårotation. Forskningen visar även att regleringen kan få en motsatt effekt på marknads- dynamiken än vad som önskas. Majoriteten av forskningen visar också att revi- sorns oberoende inte påverkas vid ett införande av obligatorisk byrårotation. I den del av vår studie som behandlade hur väl internationell forskning skulle kunna tillämpas i Sverige fann vi inga belägg på att skillnaderna mellan de un- dersökta länderna och Sverige skulle vara tillräckligt stora för att kunna ifråga- sätta dess applicerbarhet i ett svenskt sammanhang. June 17, 2016, is the final date for Sweden to implement the new rules regard- ing mandatory audit firm rotation which will require listed and financial com- panies to change their current audit firm every 10 years. The aim of the intro- duction of mandatory audit firm rotation is that the regulation should have positive effects on the independence of auditors, audit quality and the compet- itive situation within the audit market. The aim of this study is to provide a better understanding of how auditor in- dependence and audit quality is affected by the introduction of the regulation on the basis of the available international research. Since there isn’t any Swe- dish research on the subject, we want to contribute knowledge about how well these international studies can be applied in a Swedish context. The study is based on secondary data which is collected from already existing research on mandatory audit firm rotation. The choice of approach in the study can be justified by the fact that much research already exists on the sub- ject and we wanted the data to be as neutral as possible. Our study shows that the results from international research suggest that audit quality will decrease with the introduction of such regulation and that the regu- lation may have an adverse effect on the dynamic of the audit market. A ma- jority of the research suggests that auditor independence is not affected by the introduction of mandatory audit firm rotation. In the part of our study, which dealt with how well international research is applicable in Sweden, we found no evidence that the difference between the examined countries and Sweden would be large enough to allow us to question the research applicability in a Swedish context.

Marginalized sigma-point filtering

In this paper we present a method for estimatingmean and covariance of a transformed Gaussian random variable.The method is based on evaluations of the transformingfunction and resembles the unscented transform or Gauss–Hermite integration in that aspect. However, the informationprovided by the evaluations is used in a Bayesian frameworkto form a posterior description of the transforming function.Estimates are then derived by marginalizing the function fromthe analytical expression of the mean and covariance. An estimationalgorithm, based on the assumption that the transformingfunction is constructed by Hermite polynomials, is presented andcompared to the cubature rule and the unscented transform. Contraryto the unscented transform, the resulting approximation ofthe covariance matrix are guaranteed to be positive-semidefiniteand the algorithm performs much better than the cubature rulefor the evaluated scenario

A Probabilistic Framework for Decision-Making in Collision Avoidance Systems

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 evaluating the driver acceptance of an intervention and the necessity thereof to automatically avoid a collision. The intervention acceptance is modeled as high if it estimated that the driver judges the situation as critical, based on the driver's observations and predictions of the traffic situation. One advantage with the proposed framework is that interventions can be initiated at an earlier stage when the estimated driver acceptance is high. Using a simplified driver model, the framework is applied to a few different types of collision scenarios. The results show that the framework has appealing properties, both with respect to increasing the system benefit and to decreasing the risk of unnecessary interventions