| Research on Synchronous Active Collision-Avoidance Control Method for Intelligent Vehicles Based on Extended Driving Risk Assessment Model |
| Pingli Ren1,3, Haobin Jiang2, Xian Xu1, Ze Liu2, Feng Wang2 |
1Automotive Engineering Research Intstitute, Jiangsu University, Zhenjiang, 212013, China
2School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China
3School of Traffic Engineering, Changzhou Vocational Institute of Mechatronic Technology, 26 Mingxin Road, Wujin District, Changzhou, 213164, China |
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Received: February 4, 2025; Revised: May 29, 2025 Accepted: July 4, 2025. Published online: August 27, 2025. |
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| ABSTRACT |
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To enhance the precision of decision making and the ability of intelligent vehicles to prevent collisions within a vehicle-infrastructure cooperative environment, this study proposes a synchronous active collision-avoidance control method based on an extended driving risk assessment model. Initially, an extended driving risk assessment model, which describes the interaction between the host vehicle and the surrounding environment, is established by incorporating the vehicle’s inherent parameters, motion state parameters, and the inverse time to collision (ITC) indicator. Furthermore, based on NMPC method, the collision-avoidance issue is reformulated as a multiconstraint optimization problem that integrates velocity planning, trajectory planning, and trajectory tracking. This approach is based on the extended model for driving risk assessment. The objectives are to ensure collision-free operation, maintain an expected velocity when obstacles are present, and guarantee vehicle stability, ultimately generating the optimal trajectory for effective tracking control. Finally, the effectiveness of the proposed method is validated based on the combined simulation platform of Matlab/Simulink, Carsim, and Prescan. A comparative analysis is conducted with the proposed method, the hierarchical control method, and the synchronous control method using the existing driving risk assessment model. The findings indicate that the suggested method ensures smooth trajectory and stable driving for intelligent vehicles during collision avoidance. It also overcomes the shortcomings of existing risk assessment models and improves the safety of intelligent vehicles while avoiding collisions in complex traffic scenarios. |
| Key Words:
Synchronous active collision avoidance · Driving risk assessment · Intelligent vehicles · Inverse time to collision (ITC) |
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Corresponding Author.
Haobin Jiang , Email. 
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