  Anomaly driving detection is an important problem in advanced driver
assistance systems (ADAS). It is important to identify potential hazard
scenarios as early as possible to avoid potential accidents. This study
proposes an unsupervised method to quantify driving anomalies using a
conditional generative adversarial network (GAN). The approach predicts
upcoming driving scenarios by conditioning the models on the previously
observed signals. The system uses the difference of the output from the
discriminator between the predicted and actual signals as a metric to quantify
the anomaly degree of a driving segment. We take a driver-centric approach,
considering physiological signals from the driver and controller area
network-Bus (CAN-Bus) signals from the vehicle. The approach is implemented
with convolutional neural networks (CNNs) to extract discriminative feature
representations, and with long short-term memory (LSTM) cells to capture
temporal information. The study is implemented and evaluated with the driving
anomaly dataset (DAD), which includes 250 hours of naturalistic recordings
manually annotated with driving events. The experimental results reveal that
recordings annotated with events that are likely to be anomalous, such as
avoiding on-road pedestrians and traffic rule violations, have higher anomaly
scores than recordings without any event annotation. The results are validated
with perceptual evaluations, where annotators are asked to assess the risk and
familiarity of the videos detected with high anomaly scores. The results
indicate that the driving segments with higher anomaly scores are more risky
and less regularly seen on the road than other driving segments, validating the
proposed unsupervised approach.
