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  • 1.
    Eidehall, Andreas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    An Automotive Lane Guidance System2004Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Automotive lane guidance systems, usually referred to as Lane Keeping Aid or Lane Keeping System, are designed to prevent or warn the driver of lane departure. They typically use a buzzer to alert the driver or a steering wheel torque to actually steer the vehicle back into the center of the lane. Emergency Lane Assist (ELA) combines conventional lane guidance systems with a threat assessment module that tries to activate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure manoeuvres.

    Such a threat assessment algorithm is dependent on detailed information about the vehicle surroundings, i.e., positions and motion of other vehicles, but also information about road and lane geometry parameters such as lane width and road curvature. The thesis demonstrates that the lane estimate can be improved by using an integrated filter that combines information from object and lane tracking. This is done by introducing a road aligned, curved coordinate system which also brings other advantages when it comes to modelling and prediction.

    Evaluation of the integrated tracking system has been carried out on real data and the ELA decision algorithm has been tested in a demonstrator. ELA successfully distinguishes between dangerous and safe lane changes ona small set of test scenarios and is, if activated, able to take control of the vehicle and put it in a safe position in the original lane.

  • 2.
    Eidehall, Andreas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Tracking and threat assessment for automotive collision avoidance2007Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis is concerned with automotive active safety, and a central theme is a new safety function called Emergency Lane Assist (ELA). Automotive safety is often categorised into passive and active safety, where passive safety is concerned with reducing the effects of accidents and active safety aims at avoiding them. ELA detects lane departure manoeuvres that are likely to result in a collision and prevents them by applying a steering wheel torque. The ELA concept is based on traffic accident statistics, i.e., it is designed to give

    maximum safety based on information about real life traffic accidents.

    The ELA function puts tough requirements on the accuracy of the information from the sensors, in particular the road shape and the position of surrounding objects, and on robust threat assessment. Several signal processing methods have been developed and evaluated

    in order to improve the accuracy of the sensor information, and these improvements are also analysed in how they relate to the ELA requirements. Different threat assessment methods are also studied, and a common element in both the signal processing and the threat assessment is that they are based on driver behaviour models, i.e., they utilise the fact that depending on the traffic situation, drivers are more likely to behave in certain ways than others.

    Most of the methods are general and can be, and hopefully also will be, applied also in other safety systems, in particular when a complete picture of the vehicle surroundings is considered, including information about road and lane shape together with the position of vehicles and infrastructure.

    All methods in the thesis have been evaluated on authentic sensor data from actual and relevant traffic environments.

  • 3.
    Eidehall, Andreas
    et al.
    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.
    Combined Road Prediction and Target Tracking in Collision Avoidance2004In: Proceedings of the 2004 IEEE Intelligent Vehicles Symposium, 2004, p. 619-624Conference paper (Refereed)
    Abstract [en]

    Detection and tracking of other vehicles and lane geometry will be required for many future intelligent driver assistance systems. By integrating the estimation of these two features into a single filter, a more optimal utilization of the available information can be achieved. For example, it is possible to improve the lane curvature estimate during bad visibility by studying the motion of other vehicles. This paper derives and evaluates various approximations that are needed in order to deal with the non-linearities that are introduced by such an approach.

  • 4.
    Eidehall, Andreas
    et al.
    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.
    Combined Road Prediction and Target Tracking in Collision Avoidance2004Report (Other academic)
    Abstract [en]

    Detection and tracking of other vehicles and lane geometry will be required for many future intelligent driver assistance systems. By integrating the estimation of these two features into a single filter, a more optimal utilization of the available information can be achieved. For example, it is possible to improve the lane curvature estimate during bad visibility by studying the motion of other vehicles. This paper derives and evaluates various approximations that are needed in order to deal with the non-linearities that are introduced by such an approach.

  • 5.
    Eidehall, Andreas
    et al.
    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.
    Joint road geometry estimation and vehicle tracking: Extended version of "Combined road prediction and target tracking in collision avoidance"2004In: Proceedings of the IEEE Intelligent Vehicles Symposium, 2004, p. 619-624Conference paper (Refereed)
    Abstract [en]

    Detection and tracking of other vehicles and lane geometry will be required for many future intelligent driver assistance systems. By integrating the estimation of these two features into a single filter, a more optimal utilization of the available information can be achieved. For example, it is possible to improve the lane curvature estimate during bad visibility by studying the motion of other vehicles. This paper derives and evaluates various approximations that are needed in order to deal with the non-linearities that are introduced by such an approach.

  • 6.
    Eidehall, Andreas
    et al.
    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.
    Obtaining Reference Road Geometry Parameters from Recorded Sensor Data2006In: Proceedings of the 2006 IEEE Intelligent Vehicles Symposium, 2006, p. 256-260Conference paper (Refereed)
    Abstract [en]

    In many applications of tracking and sensing systems, reference data for tuning and verification of system performance is unavailable. In this article the problem of automotive on-line road shape estimation is discussed and a method for obtaining reference data for this application is presented. The reference data is based on a least squares curve which is fitted geometrically to the lane boundaries. It does not require any extra sensors or other hardware. It is also shown that the accuracy of the estimate is high enough to be used as a reference in most applications.

  • 7.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Petersson, Lars
    National ICT Australia Ltd, Australia.
    Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling2006Report (Other academic)
    Abstract [en]

    A stochastic threat assessment algorithm for general road scenes is presented. Vehicles behave in a manner which includes a desire to follow their intended paths comfortably and to avoid colliding with other objects. In particular, this can be used to detect indirect threats from objects that are not on a direct collision course, but may be forced into a collision course by the traffic situation. An example is when a vehicle has to swerve to avoid an obstacle and because of that the vehicle itself becomes a threat to another vehicle. The vehicles are on a direct collision course from the beginning, but the situation still poses a threat because of the obstacle. Control inputs of other vehicles are modelled as stochastic variables and the resulting statistical expressions are solved using Monte Carlo sampling. In any Monte Carlo method there is always a trade-off between accuracy, i.e., number of samples, and computational load. A further contribution of this work is a method to create denser sample sets without increasing computational load

  • 8.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Petersson, Lars
    National ICT Australia Ltd, Australia.
    Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling2008In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 9, no 1, p. 137-147Article in journal (Refereed)
    Abstract [en]

    This paper presents a threat-assessment algorithm for general road scenes. A road scene consists of a number of objects that are known, and the threat level of the scene is based on their current positions and velocities. The future driver inputs of the surrounding objects are unknown and are modeled as random variables. In order to capture realistic driver behavior, a dynamic driver model is implemented as a probabilistic prior, which computes the likelihood of a potential maneuver. A distribution of possible future scenarios can then be approximated using a Monte Carlo sampling. Based on this distribution, different threat measures can be computed, e.g., probability of collision or time to collision. Since the algorithm is based on the Monte Carlo sampling, it is computationally demanding, and several techniques are presented to increase performance without increasing computational load. The algorithm is intended both for online safety applications in a vehicle and for offline data analysis.

  • 9.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Petersson, Lars
    Autonomous Systems and Sensing Technology, National ICT Australia Limited, Canberra, Australia.
    Statistical threat assessment for general road scenes using Monte Carlo sampling: Extended version of "Threat assessment of general road scenes using Monte Carlo sampling"2006In: Proceedings of the IEEE Intelligent Transportation Systems, 2006Conference paper (Refereed)
    Abstract [en]

    A stochastic threat assessment algorithm for general road scenes is presented. Vehicles behave in a manner which includes a desire to follow their intended paths comfortably and to avoid colliding with other objects. In particular, this can be used to detect indirect threats from objects that are not on a direct collision course, but may be forced into a collision course by the traffic situation. An example is when a vehicle has to swerve to avoid an obstacle and because of that the vehicle itself becomes a threat to another vehicle. The vehicles are on a direct collision course from the beginning, but the situation still poses a threat because of the obstacle. Control inputs of other vehicles are modelled as stochastic variables and the resulting statistical expressions are solved using Monte Carlo sampling. In any Monte Carlo method there is always a trade-off between accuracy, i.e., number of samples, and computational load. A further contribution of this work is a method to create denser sample sets without increasing computational load

  • 10.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Petersson, Lars
    National ICT Australia.
    Threat Assessment of General Road Scenes using Monte Carlo Sampling2006In: Proceedings of the 2006 IEEE Intelligent Transportation Systems, 2006, p. 1173-1178Conference paper (Refereed)
    Abstract [en]

    A stochastic threat assessment algorithm for general road scenes is presented. Vehicles behave in a manner which includes a desire to follow their intended paths comfortably and to avoid colliding with other objects. In particular, this can be used to detect indirect threats from objects that are not on a direct collision course, but may be forced into a collision course by the traffic situation. An example is when a vehicle has to swerve to avoid an obstacle and because of that the vehicle itself becomes a threat to another vehicle. The vehicles are on a direct collision course from the beginning, but the situation still poses a threat because of the obstacle. Control inputs of other vehicles are modelled as stochastic variables and the resulting statistical expressions are solved using Monte Carlo sampling. In any Monte Carlo method there is always a trade-off between accuracy, i.e., number of samples, and computational load. A further contribution of this work is a method to create denser sample sets without increasing computational load.

  • 11.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A New Approach to Lane Guidance Systems2005In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, 2005, p. 108-112Conference paper (Refereed)
    Abstract [en]

    This paper presents a new automotive safety function called Emergency Lane Assist (ELA). ELA combines conventional lane guidance systems with a threat assessment module that tries to activate and deactivate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure manoeuvres. Such a threat assessment algorithm is dependent on detailed information about the vehicle surroundings, i.e., positions and motion of other vehicles, but also information about road and lane geometry parameters such as lane width and road curvature. An Extended Kalman Filter for estimating these parameters is used and the performance is improved by introducing a non-linear model which uses a road aligned, curved coordinate system. The ELA decision algorithm has been tested in a demonstrator and it successfully distinguishes between dangerous and safe lane changes on a small set of test scenarios. It is also able to take control of the vehicle and put it in a safe position in the original lane.

  • 12.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    A New Approach to Lane Guidance Systems2005Report (Other academic)
    Abstract [en]

    This paper presents a new automotive safety function called Emergency Lane Assist (ELA). ELA combines conventional lane guidance systems with a threat assessment module that tries to activate and deactivate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure manoeuvres. Such a threat assessment algorithm is dependent on detailed information about the vehicle surroundings, i.e., positions and motion of other vehicles, but also information about road and lane geometry parameters such as lane width and road curvature. An Extended Kalman Filter for estimating these parameters is used and the performance is improved by introducing a non-linear model which uses a road aligned, curved coordinate system. The ELA decision algorithm has been tested in a demonstrator and it successfully distinguishes between dangerous and safe lane changes on a small set of test scenarios. It is also able to take control of the vehicle and put it in a safe position in the original lane.

  • 13.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Volvo Car Corporation, Sweden.
    Joint Road Geometry Estimation and Vehicle Tracking2007Report (Other academic)
    Abstract [en]

    Detection and tracking of other vehicles and estimation of lane geometry will be required for many intelligent driver assistance systems in the future. By combining the processing of these two features into a single filter, better utilisation of the available information can be achieved. For instance, it is demonstrated that it is possible to improve the road shape estimate by including information about the lateral movement of leading vehicles. Statistical evaluation is done by comparing the estimated parameters to true values in varying road and weather conditions. The performance is also related to typical requirements of active safety applications such as adaptive cruise control and a new safety function called emergency lane assist.

  • 14.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Joint Road Geometry Estimation and Vehicle Tracking2007In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 15, no 12, p. 1484-1494Article in journal (Refereed)
    Abstract [en]

    Detection and tracking of other vehicles and estimation of lane geometry will be required for many intelligent driver assistance systems in the future. By combining the processing of these two features into a single filter, better utilisation of the available information can be achieved. For instance, it is demonstrated that it is possible to improve the road shape estimate by including information about the lateral movement of leading vehicles. Statistical evaluation is done by comparing the estimated parameters to true values in varying road and weather conditions. The performance is also related to typical requirements of active safety applications such as adaptive cruise control and a new safety function called emergency lane assist.

  • 15.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ekmark, Jonas
    Volvo Car Corporation, Sweden.
    Toward Autonomous Collision Avoidance by Steering2007Report (Other academic)
    Abstract [en]

    This paper presents a new automotive safety function called Emergency Lane Assist (ELA). ELA combines conventional lane guidance systems with a threat assessment module that tries to activate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure maneuvers. The ELA safety function is based on a statistical method that evaluates a list of safety concepts and tries to maximize the impact on accident statistics while minimizing development and hardware component costs. ELA runs in a demonstrator and successfully intervenes during lane changes that are likely to result in a collision and is also able to take control of the vehicle and return it to a safe position in the original lane. It has also been tested on 2000 km of roads in traffic without giving any false interventions

  • 16.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Pohl, Jochen
    Volvo Car Corporation, Sweden.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Ekmark, Jonas
    Volvo Car Corporation, Sweden.
    Toward Autonomous Collision Avoidance by Steering2007In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, p. 84-94Article in journal (Refereed)
    Abstract [en]

    This paper presents a new automotive safety function called Emergency Lane Assist (ELA). ELA combines conventional lane guidance systems with a threat assessment module that tries to activate the lane guidance interventions according to the actual risk level of lane departure. The goal is to only prevent dangerous lane departure maneuvers. The ELA safety function is based on a statistical method that evaluates a list of safety concepts and tries to maximize the impact on accident statistics while minimizing development and hardware component costs. ELA. runs in a demonstrator and successfully intervenes during lane changes that are likely to result in a collision and is also able to take control of the vehicle and return it to a safe position in the original lane. It has also been tested on 2000 km of roads in traffic without giving any false interventions.

  • 17.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    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.
    The Marginalized Particle Filter for Automotive Tracking Applications2005Report (Other academic)
    Abstract [en]

    This paper deals with the problem of estimating the vehicle surroundings (lane geometry and the position of other vehicles), which is needed for intelligent automotive systems, such as adaptive cruise control, collision avoidance and lane guidance. This results in a nonlinear estimation problem. For automotive tracking systems, these problems are traditionally handled using the extended Kalman filter. In this paper we describe the application of the marginalized particle filter to this problem. Studies using both synthetic and authentic data show that the marginalized particle filter can in fact give better performance than the extended Kalman filter. However, the computational load is higher.

  • 18.
    Eidehall, Andreas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    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.
    The Marginalized Particle Filter for Automotive Tracking Applications2005In: Proceedings of the 2005 IEEE Intelligent Vehicles Symposium, 2005, p. 370-375Conference paper (Refereed)
    Abstract [en]

    This paper deals with the problem of estimating the vehicle surroundings (lane geometry and the position of other vehicles), which is needed for intelligent automotive systems, such as adaptive cruise control, collision avoidance and lane guidance. This results in a nonlinear estimation problem. For automotive tracking systems, these problems are traditionally handled using the extended Kalman filter. In this paper we describe the application of the marginalized particle filter to this problem. Studies using both synthetic and authentic data show that the marginalized particle filter can in fact give better performance than the extended Kalman filter. However, the computational load is higher.

  • 19.
    Schön, Thomas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Eidehall, Andreas
    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.
    Lane Departure Detection for Improved Road Geometry Estimation2005Report (Other academic)
    Abstract [en]

    An essential part of future collision avoidance systems is to be able to predict road curvature. This can be based on vision data, but the lateral movement of leading vehicles can also be used to support road geometry estimation. This paper presents a method for detecting lane departures, including lane changes, of leading vehicles. This information is used to adapt the dynamic models used in the estimation algorithm in order to accommodate for the fact that a lane departure is in progress. The goal is to improve the accuracy of the road geometry estimates, which is affected by the motion of leading vehicles. The significantly improved performance is demonstrated using sensor data from authentic traffic environments.

  • 20.
    Schön, Thomas
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Eidehall, Andreas
    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.
    Lane Departure Detection for Improved Road Geometry Estimation2006In: Proceedings of the 2006 IEEE Intelligent Vehicle Symposium, 2006, p. 546-551Conference paper (Refereed)
    Abstract [en]

    An essential part of future collision avoidance systems is to be able to predict road curvature. This can be based on vision data, but the lateral movement of leading vehicles can also be used to support road geometry estimation. This paper presents a method for detecting lane departures, including lane changes, of leading vehicles. This information is used to adapt the dynamic models used in the estimation algorithm in order to accommodate for the fact that a lane departure is in progress. The goal is to improve the accuracy of the road geometry estimates, which is affected by the motion of leading vehicles. The significantly improved performance is demonstrated using sensor data from authentic traffic environments.

1 - 20 of 20
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