This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Methodology for Threat Assessment in Cut-in Vehicle Scenarios
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 06, 2021 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: SAE WCX Digital Summit
Advanced Driver Assistance System (ADAS) has become a common standard feature assisting greater safety and fuel efficiency in the latest automobiles. Yet some ADAS systems fail to improve driving comfort for vehicle occupants who expect human-like driving. One of the more difficult situations in ADAS-assisted driving involves instances with cut-in vehicles. In vehicle control, determining the moment at which the system recognizes a cut-in vehicle as an active target is a challenging task. A well-designed comprehensive threat assessment developed for cut-in vehicle driving scenarios should eliminate abrupt and excessive deceleration of the vehicle and produce a smooth and safe driving experience. This paper proposes a novel methodology for threat assessment for driving instances involving a cut-in vehicle. The methodology takes into consideration kinematics, vehicle dynamics, vehicle stability, road condition, and driving comfort. General kinematics have been used to define the longitudinal threat assessment level and the performance of external disturbance attenuation in robust control theory has been used to define the lateral threat assessment level. Subsequently the threat assessment level has been analyzed in various cut-in vehicle scenarios. The simulation results validate the effectiveness of the proposed method for threat assessment in response to cut-in vehicles. The proposed threat assessment method will help further develop safe and smooth ADAS reactions to cut-in vehicles.
CitationKim, S., Wang, J., Salaani, K., Rao, S. et al., "A Methodology for Threat Assessment in Cut-in Vehicle Scenarios," SAE Technical Paper 2021-01-0873, 2021, https://doi.org/10.4271/2021-01-0873.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Davidse , R.J. Older Drivers and ADAS: Which Systems Improve Road Safety? OATSS Research 30 1 6 20 2006
- Polychronopoulos , A. , Tsogas , M. , Amditis , A. , Scheunert , U. , Andreone , L. , and Tango , F. Dynamic Situation and Threat Assessment for Collision Warning Systems: The EUCLIDE Approach IEEE Intelligent Vehicles Symposium 636 641 2004
- Karlsson , R. , Jansson , J. , and Gustafsson , F. Model-Based Statistical Tracking and Decision Making for Collision Avoidance Application Proceedings of the IEEE American Control Conference 4 3435 3440 2004
- Leonard , J. , How , J. , Teller , S. et al. A Perception-Driven Autonomous Urban Vehicle Journal of Field Robotics 25 10 727 774 2008
- Transportation Research Board of the National Academies of Science 2013
- Schnelle , S. , Wang , J. , Su , H. , and Jagacinski , R. A Driver Steering Model with Personalized Desired Path Generation IEEE Transactions on Systems, Man and Cybernetics: Systems 47 1 111 120 2017
- Kim , S. , Wang , J. , Heydinger , G.J. , and Guenther , D.A. Feasibility-Based and Personalized Crash Imminence Detection and Control in Braking Situations 2019 American Control Conference (ACC) 5097 5102 2019
- Aoude , G.S. , Luders , B.D. , Lee , K.K.H. , Levine , D.S. , and How , J.P. Threat Assessment Design for Driver Assistance System at Intersections 13th International IEEE Conference on Intelligent Transportation Systems 1855 1862 2010
- Dahl , J. , de Campos , G.R. , Olsson , C. , and Fredriksson , J. Collision Avoidance: A Literature Review on Threat-Assessment Techniques IEEE Transactions on Intelligent Vehicles 4 1 101 113 2019
- Kim , S. , Wang , J. , Guenther , D. , Heydinger , G. et al. Analysis of Human Driver Behavior in Highway Cut-in Scenarios SAE Technical Paper, 2017-01-1402
- Kim , S. , Wang , J. , Heydinger , G.J. , and Guenther , D.A. The Criticality Index Development for Steering Evasive Maneuver Based on Mixed H2/H∞ Control with Parameter Uncertainties 2019 American Control Conference (ACC) 3963 3968 2019
- Hu , C. , Jing , H. , Wang , R. , Yan , F. , and Chadli , M. Robust Output-Feedback Control for Path Following of Autonomous Ground Vehicles Mech. Syst. Signal Process. 70-71 414 427 2016
- Hassibi , A. , How , J. , and Boyd , S. A Path-Following Method for Solving BMI Problems in Control 1999 American Control Conference 2 1385 1389 1999