Abstract
In vehicles with driving assistance systems, the responsibility of driving still lies with the driver. Therefore, active safety systems follow a design principle of avoiding intervention as long as possible. Decision making using this principle requires collision avoidance with surrounding objects during the evasive maneuvering of an ego vehicle. However, general decision-making methods for collision avoidance focus on preventing collisions with objects in front of the subject vehicle; the risk of collision with the following vehicle caused by emergency braking is not considered. This paper presents decision-making methods for such collision avoidance systems, wherein emergency braking and steering are considered as collision avoidance maneuvers. The risk of collision is predicted based on the braking model of a normal driver and several emergency braking strategies are designed to avoid collisions. A within-lane steering avoidance method to improve avoidance performance in small overlap collision scenarios and a general avoidance method of steering to change lanes are designed. Collisions with surrounding objects can be avoided using the designed evasive maneuvers; further, maneuvers that can be implemented at the last moment are determined. The results of computer simulations indicate an improved collision avoidance performance using the proposed methods.
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References
Ackermann, C., Isermann, R., Min, S. and Kim, C. (2014). Collision avoidance with automatic braking and swerving. IFAC Proc. Volume 47, 3, 10694–10699.
Benterki, A., Boukhnifer, M., Judalet, V. and Choubeila, M. (2019). Prediction of surrounding vehicles lane change intention using machine learning. 10th IEEE Int. Conf. Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France.
Braeuchle, C., Flehmig, F., Rosenstiel, W. and Kropf, T. (2013). Maneuver decision for active pedestrian protection under uncertainty. 16th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), The Hague, The Netherlands.
Brännström, M. and Coelingh, E. (2014). Decision-making on when to brake and when to steer to avoid a collision. Int. J. Vehicular Safety 7, 1, 87–106.
Cabrera, A., Gowal, S. and Martinoli, A. (2012). A new collision warning system for lead vehicles in rear-end collisions. IEEE Intelligent Vehicles Symp. (IV), Madrid, Spain.
Chee, W. and Tomizuka, M. (1994). Vehicle lane change maneuver in automated highway systems. UC Berkeley. California PATH Research Report. UCB-ITS-PRR-94-22.
ECE/TRANS/WP.29/GRVA (2019). Proposal for a Supplement 1 to the original text of UN Regulation No. [152] on uniform provisions concerning the approval of motor vehicles with regard to the Advanced Emergency Braking System (AEBS) for M1 and N1 vehicles.
Euro NCAP (2017). Euro NCAP 20225 Roadmap: In pursuit of vision zero.
Fausten, M. (2010). Accident avoidance by evasive maneuvers. In 4. Tagung Fahrerassistenz.
Fildes, B., Keall, M., Bos, N., Lie, A., Page, Y., Pastor, C., Pennisi, L., Rizzi, M., Thomas, P. and Tingvall, C. (2015). Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes. Accident Analysis and Prevention, 81, 24–29.
Hwang, Y. and Choi, S.B. (2018). Adaptive collision avoidance using road friction information. IEEE Trans. Intelligent Transportation Systems 20, 1, 348–361.
Isermann, R., Mannale, R. and Schmitt, K. (2012). Collision-avoidance systems PRORETA: Situation analysis and intervention control. Control Engineering Practice 20, 11, 1236–1246.
KoROAD (2020). Road traffic accidents in Korea 2019.
Kuehn, M., Hummel, T. and Bende, J. (2013). Small overlap frontal impacts involving passenger cars in Germany. Proc. 23rd Int. Technical Conf. the Enhanced Safety Vehicles (ESV), Seoul, Korea.
Lee, H. K., Shin, S. G. and Kwon, D. S. (2017). Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion. Int. J. Automotive Technology 18, 6, 1067–1076.
Lefèvre, S., Vasquez, D. and Laugier, C. (2014). A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J. 1, 1, 1–14.
Li, M., Straub, F., Kunert, M., Henze, R. and Küçükay, F. (2018). A novel cost function for decision-making strategies in automotive collision avoidance systems. IEEE Int. Conf. Vehicular Electronics and Safety (ICVES), Madrid, Spain.
Lopez, A., Sherony, R., Chien, S., Li, L., Qiang, Y. and Chen, Y. (2015). Analysis of the braking behaviour in pedestrian automatic emergency braking. IEEE 18th Int. Conf. Intelligent Transportation Systems (ITSC), Las Palmas de Gran Canaria, Spain.
Milliken, W. F. and Milliken, D. L. (1994). Race Car Vehicle Dynamics. SAE International. Warrendale, Pennsylvania, USA.
National Highway Traffic Safety Administration (NHTSA) (2007). Analyses of rear-end crashes and near-crashes in the 100-car naturalistic driving study to support rear-signaling countermeasure development. DOT HS 810 846.
National Transportation Safety Board (2015). The use of forward collision avoidance systems to prevent and mitigate rear-end crashes. Special Investigation Report NTSB/SIR-15/01 PB2015-104098.
Soudbakhsh, D. and Eskandarian, A. (2010). A collision avoidance steering controller using linear quadratic regulator. SAE Paper No. 2010-01-0459.
STATS 19 (2015). UK Road Accidents Safety Data. Retrieved from https://data.gov.uk/dataset/road-accidents-safety-data
Vangi, D., Virga, A. and Gulino, M. S. (2019). Combined activation of braking and steering for automated driving systems: adaptive intervention by injury risk-based criteria. Procedia Structural Integrity, 24, 423–436.
Wang, X., Zhu, M., Chen, M. and Tremont, P. (2016). Drivers’ rear end collision avoidance behaviors under different levels of situational urgency. Transportation Research Part C: Emerging Technologies, 71, 419–433.
Zhang, Y., Lin, Q., Wang, J., Verwer, S. and Dolan, J. M. (2018). Lane-change intention estimation for car-following control in autonomous driving. IEEE Trans. Intelligent Vehicles 3, 3, 276–286.
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This research was supported by the Ministry of Trade, Industry and Energy of Korea (Grant No. 10044775 and 20015091).
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Shin, SG., Lee, HK. & You, SH. Decision Making for Collision Avoidance Systems Considering a Following Vehicle. Int.J Automot. Technol. 24, 421–434 (2023). https://doi.org/10.1007/s12239-023-0035-4
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DOI: https://doi.org/10.1007/s12239-023-0035-4