Emergency Collision Avoidance by Steering in Critical Situations
In this paper, an emergency collision avoidance system is proposed by including not only braking but also steering control actions. The minimum distance to avoid collision is calculated separately for braking and steering based on the relative motion to the surrounding vehicles and the lane informat...
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| Published in | International journal of automotive technology Vol. 22; no. 1; pp. 173 - 184 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
The Korean Society of Automotive Engineers
01.02.2021
Springer Nature B.V 한국자동차공학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1229-9138 1976-3832 |
| DOI | 10.1007/s12239-021-0018-2 |
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| Abstract | In this paper, an emergency collision avoidance system is proposed by including not only braking but also steering control actions. The minimum distance to avoid collision is calculated separately for braking and steering based on the relative motion to the surrounding vehicles and the lane information obtained through the vision sensor. For steering avoidance control, an optimal control input is calculated through the model predictive control that satisfies constraints such as safe avoidance region created by surrounding vehicles and capacity of the vehicle actuator. In particular, for avoiding collision by lane changing, the maximum lateral acceleration and the maximum angle of the trajectory are considered. In addition, the abrupt lateral movement in avoidance causes nonlinear characteristics in tires and, thus, tire parameters are estimated through EKF (Extended Kalman Filter) to improve model prediction accuracy. The control intervention time of avoidance maneuvering is determined for braking and steering, respectively. The simulation results demonstrate that the proposed algorithm of integrating AEB (Autonomous Emergency Braking) and AES (Autonomous Emergency Steering) can effectively avoid the collision in critical situations and that the host vehicle can still maintain the safety inside the road boundary. |
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| AbstractList | In this paper, an emergency collision avoidance system is proposed by including not only braking but also steering control actions. The minimum distance to avoid collision is calculated separately for braking and steering based on the relative motion to the surrounding vehicles and the lane information obtained through the vision sensor. For steering avoidance control, an optimal control input is calculated through the model predictive control that satisfies constraints such as safe avoidance region created by surrounding vehicles and capacity of the vehicle actuator. In particular, for avoiding collision by lane changing, the maximum lateral acceleration and the maximum angle of the trajectory are considered. In addition, the abrupt lateral movement in avoidance causes nonlinear characteristics in tires and, thus, tire parameters are estimated through EKF (Extended Kalman Filter) to improve model prediction accuracy. The control intervention time of avoidance maneuvering is determined for braking and steering, respectively. The simulation results demonstrate that the proposed algorithm of integrating AEB (Autonomous Emergency Braking) and AES (Autonomous Emergency Steering) can effectively avoid the collision in critical situations and that the host vehicle can still maintain the safety inside the road boundary. In this paper, an emergency collision avoidance system is proposed by including not only braking but also steering control actions. The minimum distance to avoid collision is calculated separately for braking and steering based on the relative motion to the surrounding vehicles and the lane information obtained through the vision sensor. For steering avoidance control, an optimal control input is calculated through the model predictive control that satisfies constraints such as safe avoidance region created by surrounding vehicles and capacity of the vehicle actuator. In particular, for avoiding collision by lane changing, the maximum lateral acceleration and the maximum angle of the trajectory are considered. In addition, the abrupt lateral movement in avoidance causes nonlinear characteristics in tires and, thus, tire parameters are estimated through EKF (Extended Kalman Filter) to improve model prediction accuracy. The control intervention time of avoidance maneuvering is determined for braking and steering, respectively. The simulation results demonstrate that the proposed algorithm of integrating AEB (Autonomous Emergency Braking) and AES (Autonomous Emergency Steering) can effectively avoid the collision in critical situations and that the host vehicle can still maintain the safety inside the road boundary. KCI Citation Count: 2 |
| Author | Huh, Kunsoo Kim, Dongchan Park, Janghee |
| Author_xml | – sequence: 1 givenname: Janghee surname: Park fullname: Park, Janghee organization: Department of Automotive Engineering, Hanyang University – sequence: 2 givenname: Dongchan surname: Kim fullname: Kim, Dongchan organization: Department of Automotive Engineering, Hanyang University – sequence: 3 givenname: Kunsoo surname: Huh fullname: Huh, Kunsoo email: khuh2@hanyang.ac.kr organization: Department of Automotive Engineering, Hanyang University |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002681721$$DAccess content in National Research Foundation of Korea (NRF) |
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| Cites_doi | 10.1007/978-3-642-04898-2_321 10.1109/ITSC.2015.69 10.1109/ITSC.2006.1707153 10.1109/IVS.2017.7995716 10.4271/2015-01-1414 10.1109/TCST.2007.894653 10.7467/KSAE.2012.20.6.126 10.1080/00423119208969994 10.1016/j.conengprac.2008.12.001 10.1109/IVS.2012.6232221 10.1109/TITS.2015.2453404 10.1109/IVS.2011.5940482 10.1186/s40648-014-0001-z 10.4271/971062 10.1109/CDC.2012.6426612 10.1109/IVS.2017.7995787 10.1007/s12239-014-0035-5 |
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| References | CamachoE FAlbaC BModel predictive control2013Berlin, GermanySpringer Science & Business Media Sledge, N. H. and Marshek, K. M. (1997). Comparison of ideal vehicle lane-change trajectories. SAE Trans. 2004–2027. Tamke, A., Dang, T. and Breuel, G. (2011). A flexible method for criticality assessment in driver assistance systems. IEEE Intelligent Vehicles Symp., 697–702. Kim, H., Cho, J., Kim, D. and Huh, K. (2017). Intervention minimized semi-autonomous control using decoupled model predictive control. IEEE Intelligent Vehicles Symp., 618–623. YoonYShinJKimH JParkYSastrySModel-predictive active steering and obstacle avoidance for autonomous ground vehiclesControl Engineering Practice200917774175010.1016/j.conengprac.2008.12.001 Sötges, S. and Althoff, M. (2015). Determining the nonexistence of evasive trajectories for collision avoidance systems. IEEE Conf. Intelligent Transportation Systems (ITSC), 956–961. Ribeiro M. I. (2004). Gaussian probability density functions: Properties and error characterization. Institute for Systems and Robotics. Lisboa, Portugal. ErlienS MFujitaSGerdesJ CShared steering control using safe envelopes for obstacle avoidance and vehicle stabilityIEEE Trans. Intelligent Transportation Systems201617244145110.1109/TITS.2015.2453404 LeeDKimSKimCHuhKDevelopment of an autonomous braking system using the predicted stopping distanceInt. J. Automotive Technology201415234134610.1007/s12239-014-0035-5 Werling, M. and Liccardo, D. (2012). Automatic collision avoidance using model-predictive online optimization. IEEE Conf. Decision and Control (CDC), 6309–6314. LefèvreSVasquezDLaugierCA survey on motion prediction and risk assessment for intelligent vehiclesRobomech J.20141111410.1186/s40648-014-0001-z Berthelot, A., Tamke, A., Dang, T. and Breuel, G. (2012). A novel approach for the probabilistic computation of time-to-collision. IEEE Intelligent Vehicles Symp. Alcalá de Henares, Spain. LeeDKimIHuhKTire lateral force estimation system using nonlinear Kalman filterTrans. Korean Society of Automotive Engineers201220612613110.7467/KSAE.2012.20.6.126 FalconePBorrelliFAsgariJTsengH EHrovatDPredictive active steering control for autonomous vehicle systemsIEEE Trans. Control Systems Technology200715356658010.1109/TCST.2007.894653 Europ NCAP (2015). 2020 Roadmap, Revision 1. https://cdn.euroncap.com/media/16472/euro-ncap-2020-roadmap-rev1-march-2015.pdf Keller, M., Haß, C., Seewald, A. and Bertram, T. (2015). A model predictive approach to emergency maneuvers in critical traffic situations. IEEE Conf. Intelligent Transportation Systems (ITSC), 369–374. GoodrichJDriving miss daisy: an autonomous chauffeur systemHouston Law Review2013511265296 Shah, J. and Benmimoun, M. (2015). Driver perceived threat and behavior in rear end collision avoidance situations. SAE Paper No. 2015-01-1414. Eckert, A., Hartmann, B., Sevenich, M. and Rieth, P. E. (2011). Emergency steer & brake assist: a systematic approach for system integration of two complementary driver assistance systems. Proc. 22nd Int. Technical Conf. Enhanced Safety of Vehicles. Washington DC, USA. Grewal, M. S. (2011). Kalman filtering. Int. Encyclopedia of Statistical Science: Springer, 705–708. PacejkaH BBakkerEThe magic formula tyre modelVehicle System Dynamics19922111810.1080/00423119208969994 Carvalho, A., Gao, Y., Lefevre, S. and Borrelli, F. (2014). Stochastic predictive control of autonomous vehicles in uncertain environments. 12th Int. S. Advanced Vehicle Control (AVEC’14). Tokyo, Japan. Schram, R., Williams, A. and van Ratingen, M. (2013). Implementation of Autonomous Emergency Braking (AEB), the next step in Euro NCAP’S safety assessment. ESV, Seoul. Liu, C., Lee, S., Varnhagen, S. and Tseng, H. E. (2017). Path planning for autonomous vehicles using model predictive control. IEEE Intelligent Vehicles Symp., 174–179. Schmidt, C., Oechsle, F. and Branz, W. (2006). Research on trajectory planning in emergency situations with multiple objects. IEEE Conf. Intelligent Transportation Systems (ITSC), 988–992. 18_CR9 18_CR15 18_CR6 18_CR3 18_CR4 18_CR1 18_CR19 18_CR18 18_CR17 D Lee (18_CR12) 2012; 20 18_CR11 S Lefèvre (18_CR14) 2014; 1 18_CR10 E F Camacho (18_CR2) 2013 J Goodrich (18_CR8) 2013; 51 D Lee (18_CR13) 2014; 15 18_CR24 18_CR23 S M Erlien (18_CR5) 2016; 17 P Falcone (18_CR7) 2007; 15 H B Pacejka (18_CR16) 1992; 21 18_CR22 18_CR21 18_CR20 Y Yoon (18_CR25) 2009; 17 |
| References_xml | – reference: PacejkaH BBakkerEThe magic formula tyre modelVehicle System Dynamics19922111810.1080/00423119208969994 – reference: LefèvreSVasquezDLaugierCA survey on motion prediction and risk assessment for intelligent vehiclesRobomech J.20141111410.1186/s40648-014-0001-z – reference: ErlienS MFujitaSGerdesJ CShared steering control using safe envelopes for obstacle avoidance and vehicle stabilityIEEE Trans. Intelligent Transportation Systems201617244145110.1109/TITS.2015.2453404 – reference: Ribeiro M. I. (2004). Gaussian probability density functions: Properties and error characterization. Institute for Systems and Robotics. Lisboa, Portugal. – reference: Eckert, A., Hartmann, B., Sevenich, M. and Rieth, P. E. (2011). Emergency steer & brake assist: a systematic approach for system integration of two complementary driver assistance systems. Proc. 22nd Int. Technical Conf. Enhanced Safety of Vehicles. Washington DC, USA. – reference: YoonYShinJKimH JParkYSastrySModel-predictive active steering and obstacle avoidance for autonomous ground vehiclesControl Engineering Practice200917774175010.1016/j.conengprac.2008.12.001 – reference: Kim, H., Cho, J., Kim, D. and Huh, K. (2017). Intervention minimized semi-autonomous control using decoupled model predictive control. IEEE Intelligent Vehicles Symp., 618–623. – reference: FalconePBorrelliFAsgariJTsengH EHrovatDPredictive active steering control for autonomous vehicle systemsIEEE Trans. Control Systems Technology200715356658010.1109/TCST.2007.894653 – reference: Liu, C., Lee, S., Varnhagen, S. and Tseng, H. E. (2017). Path planning for autonomous vehicles using model predictive control. IEEE Intelligent Vehicles Symp., 174–179. – reference: Keller, M., Haß, C., Seewald, A. and Bertram, T. (2015). A model predictive approach to emergency maneuvers in critical traffic situations. IEEE Conf. Intelligent Transportation Systems (ITSC), 369–374. – reference: Sötges, S. and Althoff, M. (2015). Determining the nonexistence of evasive trajectories for collision avoidance systems. IEEE Conf. Intelligent Transportation Systems (ITSC), 956–961. – reference: LeeDKimSKimCHuhKDevelopment of an autonomous braking system using the predicted stopping distanceInt. J. Automotive Technology201415234134610.1007/s12239-014-0035-5 – reference: CamachoE FAlbaC BModel predictive control2013Berlin, GermanySpringer Science & Business Media – reference: Carvalho, A., Gao, Y., Lefevre, S. and Borrelli, F. (2014). Stochastic predictive control of autonomous vehicles in uncertain environments. 12th Int. S. Advanced Vehicle Control (AVEC’14). Tokyo, Japan. – reference: Grewal, M. S. (2011). Kalman filtering. Int. Encyclopedia of Statistical Science: Springer, 705–708. – reference: Berthelot, A., Tamke, A., Dang, T. and Breuel, G. (2012). A novel approach for the probabilistic computation of time-to-collision. IEEE Intelligent Vehicles Symp. Alcalá de Henares, Spain. – reference: GoodrichJDriving miss daisy: an autonomous chauffeur systemHouston Law Review2013511265296 – reference: Werling, M. and Liccardo, D. (2012). Automatic collision avoidance using model-predictive online optimization. IEEE Conf. Decision and Control (CDC), 6309–6314. – reference: Schmidt, C., Oechsle, F. and Branz, W. (2006). Research on trajectory planning in emergency situations with multiple objects. IEEE Conf. Intelligent Transportation Systems (ITSC), 988–992. – reference: Schram, R., Williams, A. and van Ratingen, M. (2013). Implementation of Autonomous Emergency Braking (AEB), the next step in Euro NCAP’S safety assessment. ESV, Seoul. – reference: Shah, J. and Benmimoun, M. (2015). Driver perceived threat and behavior in rear end collision avoidance situations. SAE Paper No. 2015-01-1414. – reference: Sledge, N. H. and Marshek, K. M. (1997). Comparison of ideal vehicle lane-change trajectories. SAE Trans. 2004–2027. – reference: LeeDKimIHuhKTire lateral force estimation system using nonlinear Kalman filterTrans. Korean Society of Automotive Engineers201220612613110.7467/KSAE.2012.20.6.126 – reference: Europ NCAP (2015). 2020 Roadmap, Revision 1. https://cdn.euroncap.com/media/16472/euro-ncap-2020-roadmap-rev1-march-2015.pdf – reference: Tamke, A., Dang, T. and Breuel, G. (2011). A flexible method for criticality assessment in driver assistance systems. IEEE Intelligent Vehicles Symp., 697–702. – ident: 18_CR9 doi: 10.1007/978-3-642-04898-2_321 – ident: 18_CR6 – ident: 18_CR22 – ident: 18_CR10 doi: 10.1109/ITSC.2015.69 – ident: 18_CR19 – ident: 18_CR3 – ident: 18_CR18 doi: 10.1109/ITSC.2006.1707153 – ident: 18_CR15 doi: 10.1109/IVS.2017.7995716 – ident: 18_CR17 – ident: 18_CR20 doi: 10.4271/2015-01-1414 – volume: 51 start-page: 265 issue: 1 year: 2013 ident: 18_CR8 publication-title: Houston Law Review – volume-title: Model predictive control year: 2013 ident: 18_CR2 – volume: 15 start-page: 566 issue: 3 year: 2007 ident: 18_CR7 publication-title: IEEE Trans. Control Systems Technology doi: 10.1109/TCST.2007.894653 – volume: 20 start-page: 126 issue: 6 year: 2012 ident: 18_CR12 publication-title: Trans. Korean Society of Automotive Engineers doi: 10.7467/KSAE.2012.20.6.126 – volume: 21 start-page: 1 year: 1992 ident: 18_CR16 publication-title: Vehicle System Dynamics doi: 10.1080/00423119208969994 – volume: 17 start-page: 741 issue: 7 year: 2009 ident: 18_CR25 publication-title: Control Engineering Practice doi: 10.1016/j.conengprac.2008.12.001 – ident: 18_CR1 doi: 10.1109/IVS.2012.6232221 – volume: 17 start-page: 441 issue: 2 year: 2016 ident: 18_CR5 publication-title: IEEE Trans. Intelligent Transportation Systems doi: 10.1109/TITS.2015.2453404 – ident: 18_CR4 – ident: 18_CR23 doi: 10.1109/IVS.2011.5940482 – volume: 1 start-page: 1 issue: 1 year: 2014 ident: 18_CR14 publication-title: Robomech J. doi: 10.1186/s40648-014-0001-z – ident: 18_CR21 doi: 10.4271/971062 – ident: 18_CR24 doi: 10.1109/CDC.2012.6426612 – ident: 18_CR11 doi: 10.1109/IVS.2017.7995787 – volume: 15 start-page: 341 issue: 2 year: 2014 ident: 18_CR13 publication-title: Int. J. 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| StartPage | 173 |
| SubjectTerms | Actuators Algorithms Automotive Engineering Braking Collision avoidance Collision dynamics Collisions Constraint modelling Emergencies Emergency steering Engineering Extended Kalman filter Lane changing Model accuracy Optimal control Parameter estimation Predictive control Vehicles 자동차공학 |
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| Title | Emergency Collision Avoidance by Steering in Critical Situations |
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| Volume | 22 |
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| ispartofPNX | International Journal of Automotive Technology, 2021, 22(1), 119, pp.173-184 |
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