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Model Predictive Control Based Multifunctional Advanced Driver-Assistance System Specialized for Rear-End Collision Avoidance

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Abstract

This paper presents the model predictive control (MPC) based multifunctional advanced driver-assistance system (MADAS) that is optimized for rear-end collision avoidance. First, the system’s operation is judged by considering the driver’s intention of avoidance and the possibility of avoiding obstacle vehicles. Once the system is activated, the lateral tire force corresponding to the driver’s steering input, which is essential for collision avoidance, is realized with the highest priority. The use of each tire friction circle is then maximized by utilizing available tire forces for braking through quadratic programming. While the MADAS ensures the lateral maneuver and deceleration of the vehicle, the system still can generate additional yaw moment calculated from the MPC, the upper level controller, to track the driver’s desired yaw rate or prevent the vehicle from becoming unstable. The nonlinearity inevitably encountered in maximizing tire forces is reflected through the extended bicycle model and the combined brushed tire model. The proposed system is verified by the vehicle dynamics software CarSim, and the simulation results show that the MADAS performs better in rear-end collision avoidance situations than conventional advanced driver-assistance systems (ADAS).

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Abbreviations

a x :

longitudinal acceleration

a y :

lateral acceleration

a x,max :

maximum longitudinal acceleration

a y,max :

maximum lateral acceleration

m :

vehicle mass

l f :

center of gravity-front axle distance

l r :

center of gravity-real axle distance

I z :

vehicle yaw moment of inertia

C x :

tire longitudinal stiffness parameter

C a :

tire lateral stiffness parameter

F z :

tire normal force

References

  • Bagloee, S. A., Tavana, M., Asadi, M. and Oliver, T. (2016). Autonomous vehicles: Challenges, opportunities, and future implications for transportation policies. J. Modern Transportation 24, 4, 284–303.

    Article  Google Scholar 

  • Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C. and Winner, H. (2014). Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent Transportation Systems Magazine 6, 4, 6–22.

    Article  Google Scholar 

  • Brannstrom, M., Sjoberg, J. and Coelingh, E. (2008). A situation and threat assessment algorithm for a rear-end collision avoidance system. IEEE Intelligent Vehicles Symposium (IV), Eindhoven, The Netherlands.

  • Choi, J., Yi, K., Suh, J. and Ko, B. (2014). Coordinated control of motor-driven power steering torque overlay and differential braking for emergency driving support. IEEE Trans. Vehicular Technology 63, 2, 566–579.

    Article  Google Scholar 

  • Choi, M., Oh, J. J. and Choi, S. B. (2013). Linearized recursive least squares methods for real-time identification of tire-road friction coefficient. IEEE Trans. Vehicular Technology 62, 7, 2906–2918.

    Article  Google Scholar 

  • Choi, M. and Choi, S. B. (2014). Model predictive control for vehicle yaw stability with practical concerns. IEEE Trans. Vehicular Technology 63, 8, 3539–3548.

    Article  Google Scholar 

  • Cui, Q., Ding, R., Wu, X. and Zhou, B. (2019). A new strategy for rear-end collision avoidance via autonomous steering and differential braking in highway driving. Vehicle System Dynamics 58, 6, 955–986.

    Article  Google Scholar 

  • Diederichs, F., Schüttke, T. and Spath, D. (2015). Driver intention algorithm for pedestrian protection and automated emergency braking systems. IEEE 18th Int. Conf. Intelligent Transportation Systems (ITSC), Gran Canaria, Spain.

  • Funke, J., Brown, M., Erlien, S. M. and Gerdes, J. C. (2016). Collision avoidance and stabilization for autonomous vehicles in emergency scenarios. IEEE Trans. Control Systems Technology 25, 4, 1204–1216.

    Article  Google Scholar 

  • He, X., Liu, Y., Lv, C., Ji, X. and Liu, Y. (2019). Emergency steering control of autonomous vehicle for collision avoidance and stabilisation. Vehicle System Dynamics 57, 8, 1163–1187.

    Article  Google Scholar 

  • Hestermeyer, T., Bongarth, W. and Dornhege, J. H. (2019). Steering features of the new Ford Focus. 9th International Munich Chassis Symposium 2018 (pp. 487–504). Springer Vieweg. Wiesbaden, Germany.

    Chapter  Google Scholar 

  • Hu, C., Wang, Z., Qin, Y., Huang, Y., Wang, J. and Wang, R. (2019). Lane keeping control of autonomous vehicles with prescribed performance considering the rollover prevention and input saturation. IEEE Trans. Intelligent Transportation Systems 21, 7, 3091–3103.

    Article  Google Scholar 

  • Huang, Y. and Chen, Y. (2020). Vehicle lateral stability control based on shiftable stability regions and dynamic margins. IEEE Trans. Vehicular Technology 69, 12, 14727–14738.

    Article  Google Scholar 

  • Inagaki, S., Kushiro, I. and Yamamoto, M. (1995). Analysis on vehicle stability in critical cornering using phase-plane method. JSAE Review 2, 16, 216.

    Google Scholar 

  • ISO 3888-1 (2018). Passenger cars-Test track for a severe lane-change manoeuvre - Part 1: Double lane-change.

  • ISO 3888-2 (2011). Passenger cars-Test track for a severe lane-change manoeuvre - Part 2: Obstacle avoidance.

  • Jansson, J. (2005). Collision Avoidance Theory: With Application to Automotive Collision Mitigation. Ph.D. Dissertation. Linköping University. Linköping, Sweden.

    Google Scholar 

  • Kim, H., Shin, K., Chang, I. and Huh, K. (2018). Autonomous emergency braking considering road slope and friction coefficient. Int. J. Automotive Technology 19, 6, 1013–1022.

    Article  Google Scholar 

  • Lee, H. and Choi, S. (2022). Development of collision avoidance system in slippery road conditions. IEEE Trans. Intelligent Transportation Systems. 23, 10, 19544–19556.

    Article  Google Scholar 

  • Lee, S. E., Llaneras, E., Klauer, S. and Sudweeks, J. (2007). Analyses of rear-end crashes and near-crashes in the 100-car naturalistic driving study to support rear-signaling countermeasure development. U.S. Department of Tranportation, National Highway Traffic Safety Administration. DOT HS 810 846.

  • Li, L., Jia, G., Ran, X., Song, J. and Wu, K. (2014). A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road. Vehicle System Dynamics 52, 2, 280–308.

    Article  Google Scholar 

  • Lie, A., Tingvall, C., Krafft, M. and Kullgren, A., 2006. The effectiveness of electronic stability control (ESC) in reducing real life crashes and injuries. Traffic Injury Prevention 7, 1, 38–43.

    Article  Google Scholar 

  • Mirzaei, M. and Mirzaeinejad, H. (2012). Optimal design of a non-linear controller for anti-lock braking system. Transportation Research Part C: Emerging Technologies, 24, 19–35.

    Article  Google Scholar 

  • Pacejka, H. B. (2006). Tyre and Vehicle Dynamics. 2nd edn. Elsevier. Oxford, UK.

    Google Scholar 

  • Rajamani, R. (2012). Vehicle Dynamics and Control. 2nd edn. Springer. London, UK.

    Book  MATH  Google Scholar 

  • Wang, L. (2009). Model Predictive Control System Design and Implementation Using MATLAB®. Springer Science & Business Media. Berlin, Germany.

    Google Scholar 

  • Yi, J., Alvarez, L. and Horowitz, R. (2002). Adaptive emergency braking control with underestimation of friction coefficient. IEEE Trans. Control Systems Technology 10, 3, 381–392.

    Article  Google Scholar 

  • Zhai, L., Sun, T. and Wang, J. (2016). Electronic stability control based on motor driving and braking torque distribution for a four in-wheel motor drive electric vehicle. IEEE Trans. Vehicular Technology 65, 6, 4726–4739.

    Article  Google Scholar 

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Acknowledgement

This research was partly supported by the BK21 FOUR Program of the National Research Foundation Korea (NRF) grant funded by the Ministry of Education (MOE); the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2020R1A2B5B01001531); the Technology Innovation Program (20014983, Development of autonomous chassis platform for a modular vehicle) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea); Autonomous Driving Technology Development Innovation Program (20018181, Development of Lv. 4+ autonomous driving vehicle platform based on point-to-point driving to logistic center for heavy trucks) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and Korea Evaluation Institute of Industrial Technology (KEIT).

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  1. Department of Mechanical Engineering, KAIST, Daejeon, 34141, Korea

    Hwangjae Lee & Seibum Ben Choi

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  1. Hwangjae Lee
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  2. Seibum Ben Choi
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Correspondence to Seibum Ben Choi.

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Lee, H., Choi, S.B. Model Predictive Control Based Multifunctional Advanced Driver-Assistance System Specialized for Rear-End Collision Avoidance. Int.J Automot. Technol. 24, 799–809 (2023). https://doi.org/10.1007/s12239-023-0066-x

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  • DOI: https://doi.org/10.1007/s12239-023-0066-x

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