TY - GEN
T1 - Multi-objective Predictive Control for Intelligent Vehicles by Considering Stability Constraints in Complex Scenarios
AU - Zhang, Yu
AU - Qin, Yechen
AU - Dong, Mingming
AU - Xu, Tao
AU - Hashemi, Ehsan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Intelligent vehicles are currently facing the challenge of improving driving stability and active safety while dealing with multiple objectives in complex traffic scenarios with multiple vehicles and various road surfaces. To overcome this, a new multi-objective control structure is proposed, which includes an integrated predictive model guided by a switching mechanism, and corresponding objective functions and constraints under different modes. The structure also coordinates multiple actuator inputs to integrate path planning and path following. The predictive model takes into account vehicle models, actuator dynamics, and the combined-effect tire model. A newly-developed quantitative risk calculation method is used to design the switching mechanism that switches among different driving modes. This helps the vehicle to improve active crash avoidance with approaching vehicles from various directions while satisfying driving stability constraints. Lastly, a driver-in-the-loop platform has been developed to validate the real-time performance and effectiveness of the proposed method under complex scenarios.
AB - Intelligent vehicles are currently facing the challenge of improving driving stability and active safety while dealing with multiple objectives in complex traffic scenarios with multiple vehicles and various road surfaces. To overcome this, a new multi-objective control structure is proposed, which includes an integrated predictive model guided by a switching mechanism, and corresponding objective functions and constraints under different modes. The structure also coordinates multiple actuator inputs to integrate path planning and path following. The predictive model takes into account vehicle models, actuator dynamics, and the combined-effect tire model. A newly-developed quantitative risk calculation method is used to design the switching mechanism that switches among different driving modes. This helps the vehicle to improve active crash avoidance with approaching vehicles from various directions while satisfying driving stability constraints. Lastly, a driver-in-the-loop platform has been developed to validate the real-time performance and effectiveness of the proposed method under complex scenarios.
KW - Crash avoidance
KW - Driver-in-the-loop platform
KW - Integrated structure
KW - Stability constraints
KW - Switching mechanism
UR - http://www.scopus.com/inward/record.url?scp=85207657380&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-66968-2_64
DO - 10.1007/978-3-031-66968-2_64
M3 - Conference contribution
AN - SCOPUS:85207657380
SN - 9783031669675
T3 - Lecture Notes in Mechanical Engineering
SP - 653
EP - 663
BT - Advances in Dynamics of Vehicles on Roads and Tracks III - Proceedings of the 28th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2023, Road Vehicles
A2 - Huang, Wei
A2 - Ahmadian, Mehdi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2023
Y2 - 21 August 2023 through 25 August 2023
ER -