预测使用长期短期记忆网络的车辆未来的曲折率值
János Kontos1,2, Balázs Kránicz3, Ágnes Vathy-Fogarassy2
1Continental Automotive Hungary Ltd., 8200 Veszprém, Hungary.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一个长期短期记忆网络,用于预测车辆的曲率,这对于电动和自动驾驶车辆的安全至关重要. 该模型使用过去的传感器数据准确地预测了向前0.2秒的斜率.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 精确的传感器信号处理对于电动移动和自动驾驶汽车的安全关键系统至关重要.
- 车辆的曲率是车辆动态的关键描述因素,对于干预策略至关重要.
- 预测曲率有助于提高车辆的稳定性和控制.
研究的目的:
- 提出一个长短期记忆 (LSTM) 网络模型,用于预测未来的车辆曲率值.
- 用各种驾驶场景的实验数据来评估模型的准确性.
- 为了证明该模型在实时曲率预测方面的能力,以提高车辆安全.
主要方法:
- 开发一种使用长短期记忆 (LSTM) 架构的神经网络模型.
- 训练,验证和测试LSTM模型的实验传感器数据从三个不同的驾驶场景.
- 使用历史车辆传感器信号 (最后0.3秒) 来预测未来的斜率 (0.2秒前).
主要成果:
- 拟议的LSTM模型在预测未来的曲率值方面取得了很高的准确性.
- 该车型的R2值在不同的驾驶场景中从0.8938到0.9719不等.
- 在混合驾驶场景中,该车型表现出强的性能,R2值为0.9624.
结论:
- 基于LSTM的神经网络模型有效地预测了高准确度的车辆曲率.
- 该模型使用过去的传感器数据预测曲率的能力对于先进的驾驶辅助系统 (ADAS) 和自动驾驶非常有价值.
- 这种预测能力有助于改善电动和自动驾驶汽车的安全和控制策略.
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