Research on Autonomous Driving Safety Planning Method Integrating Interactive Prediction Information

doi:10.3901/JME.260122

Abstract

Abstract: This paper addresses the core challenges of dynamic environmental interaction and complex behavior prediction in autonomous driving decision planning by proposing a safety planning method that integrates interaction prediction information. First, a graph attention network(GAT) is constructed to extract dynamic interaction features of agents, while a graph convolutional network (GCN) is designed to aggregate lane topology interaction features. Subsequently, by combining these interaction features with the vehicle's own planning features, the future trajectories of surrounding vehicles under the influence of the vehicle's own motion are decoded. Furthermore, within a model predictive control(MPC) framework, the method integrates predictive information through time-domain alignment to construct a global system state representation. A objective function and constraints are designed to ensure optimal vehicle action solutions. Experiments validate the approach using the real-world driving trajectory dataset INTERACTION and the CARLA simulation platform. Experimental results demonstrate that this method outperforms comparison models in key metrics such as prediction accuracy, planning safety, and traffic efficiency, particularly exhibiting stronger intent inference capabilities in interaction-dense scenarios. This research provides an interpretable solution for safe autonomous driving planning in complex dynamic traffic environments.

Key words: trajectory prediction, motion planning, interactive decision making, model predictive control

CLC Number:

U491

TANG Xiaolin, ZHANG Kunyi, CHEN Zhige, YANG Jianying, YANG Wei. Research on Autonomous Driving Safety Planning Method Integrating Interactive Prediction Information[J]. Journal of Mechanical Engineering, 2026, 62(4): 249-262.

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