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Eidehall, Andreas
Publications (10 of 20) Show all publications
Eidehall, A. & Petersson, L. (2008). Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling. IEEE transactions on intelligent transportation systems (Print), 9(1), 137-147
Open this publication in new window or tab >>Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling
2008 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 9, no 1, p. 137-147Article in journal (Refereed) Published
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.

Keyword
Decision making, Monte Carlo, Road vehicle safety, Threat assessment
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-45907 (URN)10.1109/TITS.2007.909241 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
Eidehall, A., Pohl, J. & Gustafsson, F. (2007). Joint Road Geometry Estimation and Vehicle Tracking. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Joint Road Geometry Estimation and Vehicle Tracking
2007 (English)Report (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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. p. 10
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2755
Keyword
Active safety, Centralised filtering, Extended Kalman filter, Road shape estimation, Sensor fusion, Target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56108 (URN)LiTH-ISY-R-2755 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-06-26Bibliographically approved
Eidehall, A., Pohl, J. & Gustafsson, F. (2007). Joint Road Geometry Estimation and Vehicle Tracking. Control Engineering Practice, 15(12), 1484-1494
Open this publication in new window or tab >>Joint Road Geometry Estimation and Vehicle Tracking
2007 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 15, no 12, p. 1484-1494Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2007
Keyword
Active safety, Centralised filtering, Extended Kalman filter, Road shape estimation, Sensor fusion, Target tracking
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-47689 (URN)10.1016/j.conengprac.2007.02.010 (DOI)
Note

 © 2007 Elsevier Ltd. All rights reserved.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
Eidehall, A., Pohl, J., Gustafsson, F. & Ekmark, J. (2007). Toward Autonomous Collision Avoidance by Steering. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Toward Autonomous Collision Avoidance by Steering
2007 (English)Report (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

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2007. p. 10
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2754
Keyword
Decision-making, Driver assistance, Radar signal processing, Road vehicle active safety, Road vehicle collision avoidance, Road vehicles
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56107 (URN)LiTH-ISY-R-2754 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-06-26Bibliographically approved
Eidehall, A., Pohl, J., Gustafsson, F. & Ekmark, J. (2007). Toward Autonomous Collision Avoidance by Steering. IEEE transactions on intelligent transportation systems (Print), 8(1), 84-94
Open this publication in new window or tab >>Toward Autonomous Collision Avoidance by Steering
2007 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, p. 84-94Article in journal (Refereed) Published
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.

Keyword
Decision-making, Driver assistance, Radar signal processing, Road vehicle active safety, Road vehicle collision avoidance, Road vehicles
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-45967 (URN)10.1109/TITS.2006.888606 (DOI)000244929200010 ()
Note

Extended version for "A new approach to lane guidance systems”

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
Eidehall, A. (2007). Tracking and threat assessment for automotive collision avoidance. (Doctoral dissertation). : Institutionen för systemteknik
Open this publication in new window or tab >>Tracking and threat assessment for automotive collision avoidance
2007 (English)Doctoral 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.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2007
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1066
Keyword
Active safety, collision avoidance, lane guidance, state estimation, target trackin, Kalman filter, centralized filtering, threa
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-8338 (URN)91-85643-10-6 (ISBN)
Public defence
2007-01-26, Visionen, B, ISY, 10:15 (English)
Opponent
Supervisors
Available from: 2007-02-27 Created: 2007-02-27 Last updated: 2009-04-09
Schön, T., Eidehall, A. & Gustafsson, F. (2006). Lane Departure Detection for Improved Road Geometry Estimation. In: Proceedings of the 2006 IEEE Intelligent Vehicle Symposium. Paper presented at 2006 IEEE Intelligent Vehicle Symposium, Tokyo, Japan, June, 2006 (pp. 546-551).
Open this publication in new window or tab >>Lane Departure Detection for Improved Road Geometry Estimation
2006 (English)In: Proceedings of the 2006 IEEE Intelligent Vehicle Symposium, 2006, p. 546-551Conference paper, Published 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.

Keyword
Automotive tracking, Change detection, State estimation, Kalman filter, CUSUM-test
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-13923 (URN)10.1109/IVS.2006.1689685 (DOI)
Conference
2006 IEEE Intelligent Vehicle Symposium, Tokyo, Japan, June, 2006
Available from: 2006-09-04 Created: 2006-09-04 Last updated: 2013-02-26
Eidehall, A. & Gustafsson, F. (2006). Obtaining Reference Road Geometry Parameters from Recorded Sensor Data. In: Proceedings of the 2006 IEEE Intelligent Vehicles Symposium. Paper presented at 2006 IEEE Intelligent Vehicles Symposium, Tokyo, Japan, June, 2006 (pp. 256-260).
Open this publication in new window or tab >>Obtaining Reference Road Geometry Parameters from Recorded Sensor Data
2006 (English)In: Proceedings of the 2006 IEEE Intelligent Vehicles Symposium, 2006, p. 256-260Conference paper, Published 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.

Keyword
Curve fitting, Geometry, Least squares approximations, Road traffic, Road vehicles, Tracking, Traffic engineering computing, Automotive online road shape estimation, Automotive tracking, Curve fitting, Lane geometry estimation, Lane tracking, Least squares curve, Recorded sensor data, Reference road geometry
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-14307 (URN)10.1109/IVS.2006.1689638 (DOI)
Conference
2006 IEEE Intelligent Vehicles Symposium, Tokyo, Japan, June, 2006
Available from: 2007-02-27 Created: 2007-02-27 Last updated: 2013-02-25
Eidehall, A. & Petersson, L. (2006). Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Statistical Threat Assessment for General Road Scenes using Monte Carlo Sampling
2006 (English)Report (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

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2756
Keyword
Decision making, Monte Carlo, Road vehicle safety, Threat assessment
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56109 (URN)LiTH-ISY-R-2756 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-06-26Bibliographically approved
Eidehall, A. & Petersson, L. (2006). Statistical threat assessment for general road scenes using Monte Carlo sampling: Extended version of "Threat assessment of general road scenes using Monte Carlo sampling". In: Proceedings of the IEEE Intelligent Transportation Systems: .
Open this publication in new window or tab >>Statistical threat assessment for general road scenes using Monte Carlo sampling: Extended version of "Threat assessment of general road scenes using Monte Carlo sampling"
2006 (English)In: Proceedings of the IEEE Intelligent Transportation Systems, 2006Conference paper, Published 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

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-14305 (URN)10.1109/ITSC.2006.1707381 (DOI)
Available from: 2007-02-27 Created: 2007-02-27 Last updated: 2009-02-17
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