Decision-making is one of the critical modules of autonomous vehicles, and how to respond to other agents under interactive scenarios is still a challenge. Although current decision-making methods have managed to conduct longitudinal and lateral planning, the adaption to driver feature diversity is still not sufficient, failing to consider individual driving demands. To enhance the adaptability of autonomous driving decision-making, this paper presents a personalized interactive autonomous vehicle decision-making method that integrates driving style recognition. By analyzing driver history driving trajectories from open datasets, related driving factors are extracted. To reduce the problem dimension, Principal Component Analysis and Factor Analysis are applied so that key parameters can be filtered. With the input of extracted key factors, a K-means clustering module is proposed to recognize the driving style which is classified into conservative, general, and aggressive. After identifying driver style, we design a decision-making fusion strategy that merges individual driver identification into both lateral and longitudinal control methods based on MPC and polyline planning. We validated the effectiveness of the method through real-world vehicle testing, and the results demonstrate that our method can accurately recognize driving styles, thereby achieving highly human-like autonomous driving decisions.
A Personalized Interactive Autonomous Vehicle Decision-Making Method Considering Driving Style Recognition
2024-09-24
965266 byte
Conference paper
Electronic Resource
English
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