  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.
