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Lane Departure Warning Mechanism of Limited False Alarm Rate using Extreme Learning Residual Network and ϵ-greedy LSTM.
Sensors ( IF 3.9 ) Pub Date : 2020-01-23 , DOI: 10.3390/s20030644
Qiaoming Gao 1 , Huijun Yin 1 , Weiwei Zhang 2
Affiliation  

Neglecting the driver behavioral model in lane-departure-warning systems has taken over as the primary reason for false warnings in human-machine interfaces. We propose a machine learning-based mechanism to identify drivers' unintended lane-departure behaviors, and simultaneously predict the possibility of driver proactive correction after slight departure. First, a deep residual network for driving state feature extraction is established by combining time series sensor data and three serial ReLU residual modules. Based on this feature network, online extreme learning machine is organized to identify a driver's behavior intention, such as unconscious lane-departure and intentional lane-changing. Once the system senses unconscious lane-departure before crossing the outermost warning boundary, the ϵ-greedy LSTM module in shadow mode is roused to verify the chances of driving the vehicle back to the original lane. Only those unconscious lane-departures with no drivers' proactive correction behavior are transferred into the warning module, guaranteeing that the system has a limited false alarm rate. In addition, naturalistic driving data of twenty-one drivers are collected to validate the system performance. Compared with the basic time-to-line-crossing (TLC) method and the TLC-DSPLS method, the proposed warning mechanism shows a large-scale reduction of 12.9% on false alarm rate while maintaining the competitive accuracy rate of about 98.8%.

中文翻译:

使用极限学习残差网络和ϵ-贪婪LSTM的有限虚警率的车道偏离警告机制。

忽略车道偏离警告系统中的驾驶员行为模型已成为人机界面中错误警告的主要原因。我们提出一种基于机器学习的机制,以识别驾驶员的意外车道偏离行为,并同时预测驾驶员在轻度偏离后主动纠正的可能性。首先,通过结合时间序列传感器数据和三个串行ReLU残差模块,建立了用于驾驶状态特征提取的深度残差网络。基于此功能网络,可以组织在线极限学习机来识别驾驶员的行为意图,例如无意识的车道偏离和故意的车道变换。一旦系统在越过最外面的警告边界之前感觉到无意识的车道偏离,激活了处于阴影模式的L-贪婪LSTM模块,以验证将车辆驶回原始车道的机会。仅将那些没有驾驶员主动纠正行为的无意识车道驶入警告模块,以确保系统具有有限的误报率。另外,收集了二十一个驾驶员的自然驾驶数据以验证系统性能。与基本的穿越时间线(TLC)方法和TLC-DSPLS方法相比,该警告机制在误报率方面大幅度降低了12.9%,同时保持了约98.8%的竞争准确率。主动纠正行为被转移到警告模块中,从而确保系统具有有限的误报率。另外,收集了二十一个驾驶员的自然驾驶数据以验证系统性能。与基本的穿越时间线(TLC)方法和TLC-DSPLS方法相比,所提出的警告机制在误报率方面大幅度降低了12.9%,同时保持了约98.8%的竞争准确率。主动纠正行为被转移到警告模块中,从而确保系统具有有限的误报率。另外,收集了二十一个驾驶员的自然驾驶数据以验证系统性能。与基本的穿越时间线(TLC)方法和TLC-DSPLS方法相比,该警告机制在误报率方面大幅度降低了12.9%,同时保持了约98.8%的竞争准确率。
更新日期:2020-01-23
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