Change search
Refine search result
1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Bergman, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forsell, Urban
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Particle Filters for Positioning, Navigation and Tracking2002In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 50, no 2, p. 425-437Article in journal (Refereed)
    Abstract [en]

    A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter based algorithms. Here the use of non-linear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map, and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable to satellite navigation (as GPS), but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

  • 2.
    Gustafsson, Fredrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Bergman, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forssell, Urban
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nordlund, Per-Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Particle Filters for Positioning, Navigation and Tracking2001Report (Other academic)
    Abstract [en]

    A framework for positioning, navigation and tracking problems using particle filters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general non-linear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and the particle filter becomes low-dimensional. This is of utmost importance for high-performance real-time applications. Automotive and airborne applications illustrate numerically the advantage over classical Kalman filter based algorithms. Here the use of non-linear models and non-Gaussian noise is the main explanation for the improvement in accuracy. More specifically, we describe how the technique of map matching is used to match an aircraft's elevation profile to a digital elevation map, and a car's horizontal driven path to a street map. In both cases, real-time implementations are available, and tests have shown that the accuracy in both cases is comparable to satellite navigation (as GPS), but with higher integrity. Based on simulations, we also argue how the particle filter can be used for positioning based on cellular phone measurements, for integrated navigation in aircraft, and for target tracking in aircraft and cars. Finally, the particle filter enables a promising solution to the combined task of navigation and tracking, with possible application to airborne hunting and collision avoidance systems in cars.

  • 3.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Collision Avoidance Theory: with Application to Automotive Collision Mitigation2005Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Avoiding collisions is a crucial issue in most transportation systems as well as in many other applications. The task of a collision avoidance system is to track objects of potential collision risk and determine any action to avoid or mitigate a collision. This thesis presents theory for tracking and decision making in collision avoidance systems. The main focus is how to make decisions based on uncertain estimates and in the presence of multiple obstacles. A general framework for dealing with nonlinear dynamic systems and arbitrary noise distributions in collision avoidance decision making is proposed. Some novel decision functions are also suggested. Furthermore, performance evaluations using simulated and experimental data are presented. Most examples in this thesis are from automotive applications.

    A driving application for the work presented in this thesis is an automotive emergency braking system. This system is called a collision mitigation by braking (CMbB) system. It aims at mitigating the consequences of an accident by applying the brakes once a collision becomes unavoidable. A CMbB system providing a maximum collision speed reduction of 15 km/h and an average speed reduction of 7.5 km/h is estimated to reduce all injuries, classified as anything between moderate and fatal, for rear-end collisions by 16%. Since rear-end collision correspond to approximately 30% of all accidents this corresponds to a 5% reduction for all accidents.

    The evaluation includes results from simulations as well as two demonstrator vehicles, with different sensor setups and different decision logic, that perform autonomous emergency braking.

  • 4.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Dealing with Uncertainty in Automotive Collision Avoidance2004In: Proceedings of the 8th International Forum on Advanced Microsystems for Automotive Applications, 2004, p. 165-175Conference paper (Refereed)
    Abstract [en]

    Modern automobiles incorporate more and more active driver assist system that uses sensors and microprocessors to control the vehicles dynamics. This paper discus a driver assist system that performs autonomous braking when the vehicle is close to colliding. Decision making in such systems is inherently uncertain, due to the sensors’ measurement uncertainty and the uncertainty of the driver’s future actions. Furthermore computational capacity is limited by the microprocessors used in automotive applications. In this paper considerations for dealing with the uncertainty of the estimated parameters in the decision making process is discussed. Risk metrics and a computationally efficient method for decision making is proposed. It will be shown that under certain conditions the risk for a to early intervention can be kept constant for different closing velocities using the proposed method. Furthermore tracking and modelling of driver, sensors and brake system is discussed. The models of driver actions and of the radar sensor measurement error are based on measurements from a Collision Avoidance system equipped Volvo V70, provided by Volvo Car Corporation.

  • 5.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Tracking and decision making for automotive collision avoidance2002Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The aim of the present work is to find a stable and effective optimization algorithm that can be used to determine the location and size of drawbeads in sheet metal forming processes. The result from the optimization will be the restraining force that each drawbead applies to the blank. In addition, optimization has been used to determine the physical dimensions of the drawbead.

    The study in Paper I is limited to two structures in crashworthiness design, each with two design variables and three responses. The conclusion is that 1.5 times the minimum number of function evaluation should be used to fit the surface approximations. The conclusion of this study gives an indication on how many evaluations that are needed in order to efficiently construct the response surface approximations.

    Paper II shows that the optimization algorithm using Space Mapping is well suited for optimization problems in crashworthiness design and in the design of sheet metal forming processes. All optimization applications converged to the correct optimum and the computing time was decreased with a maximum of 63% relative to the traditional RSM optimization.

    The Space Mapping algorithm presented in Paper III converged to an optimum value that is lower than the optimum value from the traditional RSM, when the approximated surfaces from the first RSM iteration were used as the coarse model. The Space Mapping algorithm, however, reached a higher value compared to RSM, when surfaces from the third RSM iteration was used as the coarse model. Hence, the Space Mapping algorithm converged to a better objective value compared to RSM with less evaluations.

    This thesis has shown that it is possible to apply optimization on the design of sheet metal forming processes. For the problem used in the last paper both the traditional Response Surface Methodology and the Space Mapping technique were successful in avoiding failure due to necking and to decrease the risk of wrinkles. The Response Surface Methodology is robust enough to be used in the industrial tool design process. The Space Mapping technique using linear response surfaces as coarse model needs further research before it can be used efficiently in industry.

  • 6.
    Jansson, Jonas
    et al.
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Decision Making for Collision Avoidance Systems2002In: Proceedings of the Society of Automotive Engineering 2002 World Congress & Exhibition, 2002Conference paper (Refereed)
    Abstract [en]

    Driver errors cause a majority of all car accidents. Forward collision avoidance systems aim at avoiding, or at least mitigating, host vehicle frontal collisions, of which rear-end collisions are one of the most common. This is done by either warning the driver or braking or steering away, respectively, where each action requires its own considerations and design. We here focus on forward collision by braking, and present a general method for calculating the risk for collision. A brake maneuver is activated to mitigate the accident when the probability of collision is one, taking all driver actions into considerations. We describe results from a simulation study using a large number of scenarios, created from extensive accident statistics. We also show some results from an implementation of a forward collision avoidance system in a Volvo V70. The system has been tested in real traffic, and in collision scenarios (with an inflatable car) showing promising results.

  • 7.
    Karlsson, Rickard
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application2004Report (Other academic)
    Abstract [en]

    A growing research topic within the automotive industry is active safety systems. These systems aim at helping the driver avoid or mitigate the consequences of an accident. In this paper a collision mitigation system that performs late braking is discussed. The brake decision is based on estimates from tracking sensors. We use a Bayesian approach, implementing an extended Kalman filter (EKF) and a particle filter to solve the tracking problem. The two filters are compared for different sensor noise distributions in a Monte Carlo simulation study. In particular a bi-modal Gaussian distribution is proposed to model measurement noise for normal driving. For ideal test conditions the noise probability density is derived from experimental data. The brake decision is based on a statistical hypothesis test, where collision risk is measured in terms of required acceleration to avoid collision. The particle filter method handles this test easily. Since the test is not analytically solvable a stochastic integration is performed for the EKF method. Both systems perform well in the simulation study under the assumed sensor accuracy. The particle filter based algorithm is also implemented in a real-time testbed and fulfilled the on-line requirements.

  • 8.
    Nordlund, Per-Johan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Karlsson, Rickard
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Jansson, Jonas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Forssell, Urban
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Bergman, Niclas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A Framework for Particle Filtering for Positioning, Navigation and Tracking2001In: Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, IEEE , 2001, p. 34-37Conference paper (Refereed)
    Abstract [en]

    A framework for positioning, navigation and tracking problems using particle filters (recursive Monte Carlo methods) is developed. Automotive and airborne applications, approached in this framework, have proven a numerical advantage over classical Kalman filter based algorithms. Here the use of non-linear measurement models and non-Gaussian measurement noise is the main explanation for the improvement in accuracy, and models for relevant sensors are surveyed.

1 - 8 of 8
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf