| Human-Like Behavior Decision-Making for Autonomous Driving Based on Driving Expectations |
| Shuen Zhao1, Deyang Zhu1, Maojie Zhao2 |
1School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, China
2China Automotive Engineering Research Institute Company Limited, Chongqing, 401100, China |
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Received: June 30, 2025; Revised: August 12, 2025 Accepted: August 27, 2025. Published online: September 22, 2025. |
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| ABSTRACT |
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To address the current limitations of autonomous driving systems in naturally imitating human driving behavior, this study proposes a human-like behavior decision-making model that incorporates driver expectations. Driving expectations refers to a driver's subjective preferences regarding desired speed and following distance. By quantifying the deviation between these expectations and the actual driving state, cumulative dissatisfaction values for speed and car-following distance were introduced as extended features of the model. Typical behavioral and environmental features were selected from the HighD real-world highway trajectory dataset, and a combination of Spearman correlation and random forest importance analysis was employed for feature selection. On this basis, a BiLSTM-attention decision-making model was developed by integrating a bidirectional long short-term memory network and an attention mechanism. In addition, a minimum safety spacing model was incorporated to impose safety constraints on lane-changing behavior. The model was trained and evaluated using the HighD dataset. Experimental results show that it achieved an average prediction accuracy of 94.22% for driving behaviors in highway scenarios, outperforming other models while using fewer features.
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| Key Words:
Autonomous driving · Driving expectations · Behavior decision-making · BiLSTM · Attention mechanism |
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Corresponding Author.
Deyang Zhu , Email. 
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