一种基于实体学的车辆行为预测方法,包括车辆光信号检测
Xiaolong Xu1, Xiaolin Shi1, Yun Chen2
1College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究将深度学习与本体论推理相结合,以改进车辆行为预测. 这种新的方法提高了准确性和可解释性,这对于安全的自动驾驶系统至关重要.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 针对车辆行为预测的深度学习面临着整合交通规则和环境数据的挑战.
- 深度学习的黑子性质限制了解释性和实际应用.
- 本体论推理通过利用人类领域的知识提供了可解释性.
研究的目的:
- 提出一种新的前置车辆行为预测方法,将深度学习和本体论推理结合起来.
- 解决现有的深度学习方法在解释性和数据集成方面的局限性.
- 在复杂的交通场景中提高车辆行为预测的准确性和可靠性.
主要方法:
- 使用YOLOv5s与卷积块注意模块 (CBAM) 进行车灯检测.
- 综合加权双向特征金字塔网络 (BIFPN) 改善了多尺度特征平衡.
- 基于在四车道交叉点分析的因素,开发了一种用于预测车辆行为的本体模型.
主要成果:
- 改进的YOLOv5s模型在定制数据集上实现了3.9%更高的准确性和2.5%更高的mAP@0.5.
- 本体论推理成功地预测了复杂的行为,如减速,停止和向左转.
- 综合方法表明,预测准确性和解释性得到了增强.
结论:
- 提出的深度学习和本体论推理混合方法有效预测前方车辆的行为.
- 整合提高了可解释性,解决了纯粹深度学习方法的关键局限性.
- 这种方法为智能运输系统提供了实用和可靠的解决方案.
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