The deep neural networks (DNNs)based autonomous driving systems (ADSs) are expected to reduce road accidents and improve safety in the transportation domain as it removes the factor of human error from driving tasks. The DNN based ADS sometimes may exhibit erroneous or unexpected behaviors due to unexpected driving conditions which may cause accidents. It is not possible to generalize the DNN model performance for all driving conditions. Therefore, the driving conditions that were not considered during the training of the ADS may lead to unpredictable consequences for the safety of autonomous vehicles. This study proposes an autoencoder and time series analysis based anomaly detection system to prevent the safety critical inconsistent behavior of autonomous vehicles at runtime. Our approach called DeepGuard consists of two components. The first component, the inconsistent behavior predictor, is based on an autoencoder and time series analysis to reconstruct the driving scenarios. Based on reconstruction error and threshold it determines the normal and unexpected driving scenarios and predicts potential inconsistent behavior. The second component provides on the fly safety guards, that is, it automatically activates healing strategies to prevent inconsistencies in the behavior. We evaluated the performance of DeepGuard in predicting the injected anomalous driving scenarios using already available open sourced DNN based ADSs in the Udacity simulator. Our simulation results show that the best variant of DeepGuard can predict up to 93 percent on the CHAUFFEUR ADS, 83 percent on DAVE2 ADS, and 80 percent of inconsistent behavior on the EPOCH ADS model, outperforming SELFORACLE and DeepRoad. Overall, DeepGuard can prevent up to 89 percent of all predicted inconsistent behaviors of ADS by executing predefined safety guards.
翻译:深度神经网络(DNN)基于深度神经自动驾驶系统(ADS)预计会减少道路事故,提高运输领域的安全性,因为它消除了驾驶任务中人为错误的因素。基于DNN的ADS有时会因意外驾驶条件而出现错误或意外行为,这可能造成事故。不可能对所有驾驶条件都概括DNN模式的性能。因此,培训ADS期间未考虑的驾驶条件可能会对自主车辆的安全产生不可预测的后果。本研究建议以异常检测系统为基础进行自动编码和时间序列分析,以防止在运行时自动车辆发生严重的安全错失行为。我们称为DeepGuard的方法由两个部分组成。第一个组成部分,即不一致的行为预测,以自动编码和时间序列分析为基础,以重建错误和门槛为基础,确定正常和意外的驾驶方案,并预测潜在的不一致性行为。第二个组成部分是飞行安全卫士,即自动启动基于异常表现战略的不一致性战略,以防止行为不一致。我们称为DGODO的Develop GOA,在预测A-NEFFA的预结果中,我们目前显示最佳的A。