Active Collision-Avoidance Control Based on Emergency Decisions and Planning for Vehicle–Pedestrian Interaction Scenarios

Zexuan Han, Jiageng Ruan (), Ying Li, He Wan, Zhenpeng Xue and Jinming Zhang
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Zexuan Han: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100020, China
Jiageng Ruan: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100020, China
Ying Li: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100020, China
He Wan: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100020, China
Zhenpeng Xue: College of Mechanical and Energy Engineering, Beijing University of Technology, Beijing 100020, China
Jinming Zhang: School of Intelligent Manufacturing, Weifang University of Science and Technology, Weifang 262700, China

Sustainability, 2025, vol. 17, issue 5, 1-21

Abstract: Safe driving and effective collision avoidance are critical challenges in the development of autonomous driving technology. As the dynamic interactions between vehicles and pedestrians become increasingly complex, making rational decisions and accurately executing planning and control in emergency situations has become a core issue for sustainable development relating to traffic mobility and safety. This paper proposes an active collision-avoidance control strategy based on emergency decisions and planning in the context of vehicle–pedestrian interactions. A safety-distance model is developed with consideration given to the dynamic interactions between these two entities, and an emergency-decision mechanism is designed using the integration of priority rules. To generate smooth collision-avoidance trajectories, a quintic polynomial method is employed to construct trajectory clusters that meet the desired specifications. Moreover, a multi-objective optimization value function which considers multiple factors comprehensively is used to select the optimal path. To enhance collision-avoidance control accuracy, an RBF (radial basis function)–optimized SMC (sliding mode control) algorithm is introduced. Additionally, an FD-SF (force demand–based speed feedback) algorithm is designed to accurately track the longitudinal braking path. The results indicate that the proposed strategy can generate efficient, comfortable, and smooth optimal collision-avoidance paths, significantly improving vehicle response speed and control accuracy.

Keywords: intelligent vehicle; emergency decision; path planning; collision avoidance; interaction scenario; sustainable traffic (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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