虚拟测试中绕道场景的风险评估,基于改进的驾驶安全现场
Wentao Chen1, Aoxue Li2, Haobin Jiang1
1Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究引入了自动驾驶汽车改进的驾驶安全现场模型,提高了复杂的绕道场景中的风险评估. 新型车显著提高了危险情况的检测,提高了整体车辆安全.
科学领域:
- 自动驾驶自动驾驶的自动驾驶
- 智能运输系统 智能运输系统
- 车辆动力学 车辆动力学
背景情况:
- 基于场景的测试对于自动驾驶汽车至关重要.
- 传统的风险指标对于复杂的绕道场景是不够的.
- 为了安全的自主导航,需要准确的风险量化.
研究的目的:
- 开发一个改进的驾驶安全现场模型,用于量化绕道场景中的风险.
- 提高自动驾驶中危险场景检测的准确性和效率.
- 为虚拟测试提供指导,并提高自动驾驶汽车的安全性.
主要方法:
- 改进的驾驶安全现场模型,包括合并/分离过程中的车辆动态.
- 模型参数校准使用社会力量模型与rounD数据集.
- 类似DENCLUE的集群用于碰撞系统的提取和风险程度的计算.
主要成果:
- 拟议的模型准确地量化了绕道场景中的驾驶风险.
- 与碰撞时间 (TTC) 相比,危险场景的检测效率提高了175%.
- 时间和空间风险分析为虚拟测试提供了宝贵的见解.
结论:
- 改进的驾驶安全现场模型有效地解决了环形车道传统方法的局限性.
- 该方法增强了用于强大的自动驾驶汽车测试的关键场景的生成.
- 这有助于提高自动驾驶系统的安全性和可靠性.
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