Research on a Driver's Perceived Risk Prediction Model Considering Traffic Scene Interaction
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915184360030208 |
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| author | Yang, Chenhao Huang, Siwei Hu, Chuan |
| author_facet | Yang, Chenhao Huang, Siwei Hu, Chuan |
| contents | In the field of conditional autonomous driving technology, driver perceived risk prediction plays a crucial role in reducing traffic risks and ensuring passenger safety. This study introduces an innovative perceived risk prediction model for human-machine interaction in intelligent driving systems. The model aims to enhance prediction accuracy and, thereby, ensure passenger safety. Through a comprehensive analysis of risk impact mechanisms, we identify three key categories of factors, both subjective and objective, influencing perceived risk: driver's personal characteristics, ego-vehicle motion, and surrounding environment characteristics. We then propose a deep-learning-based risk prediction network that uses the first two categories of factors as inputs. The network captures the interactive relationships among traffic participants in dynamic driving scenarios. Additionally, we design a personalized modeling strategy that incorporates driver-specific traits to improve prediction accuracy. To ensure high-quality training data, we conducted a rigorous video rating experiment. Experimental results show that the proposed network achieves a 10.0% performance improvement over state-of-the-art methods. These findings suggest that the proposed network has significant potential to enhance the safety of conditional autonomous driving systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Research on a Driver's Perceived Risk Prediction Model Considering Traffic Scene Interaction Yang, Chenhao Huang, Siwei Hu, Chuan Human-Computer Interaction In the field of conditional autonomous driving technology, driver perceived risk prediction plays a crucial role in reducing traffic risks and ensuring passenger safety. This study introduces an innovative perceived risk prediction model for human-machine interaction in intelligent driving systems. The model aims to enhance prediction accuracy and, thereby, ensure passenger safety. Through a comprehensive analysis of risk impact mechanisms, we identify three key categories of factors, both subjective and objective, influencing perceived risk: driver's personal characteristics, ego-vehicle motion, and surrounding environment characteristics. We then propose a deep-learning-based risk prediction network that uses the first two categories of factors as inputs. The network captures the interactive relationships among traffic participants in dynamic driving scenarios. Additionally, we design a personalized modeling strategy that incorporates driver-specific traits to improve prediction accuracy. To ensure high-quality training data, we conducted a rigorous video rating experiment. Experimental results show that the proposed network achieves a 10.0% performance improvement over state-of-the-art methods. These findings suggest that the proposed network has significant potential to enhance the safety of conditional autonomous driving systems. |
| title | Research on a Driver's Perceived Risk Prediction Model Considering Traffic Scene Interaction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.04516 |