  Many current autonomous systems are being designed with a strong reliance on
black box predictions from deep neural networks (DNNs). However, DNNs tend to
be overconfident in predictions on unseen data and can give unpredictable
results for far-from-distribution test data. The importance of predictions that
are robust to this distributional shift is evident for safety-critical
applications, such as collision avoidance around pedestrians. Measures of model
uncertainty can be used to identify unseen data, but the state-of-the-art
extraction methods such as Bayesian neural networks are mostly intractable to
compute. This paper uses MC-Dropout and Bootstrapping to give computationally
tractable and parallelizable uncertainty estimates. The methods are embedded in
a Safe Reinforcement Learning framework to form uncertainty-aware navigation
around pedestrians. The result is a collision avoidance policy that knows what
it does not know and cautiously avoids pedestrians that exhibit unseen
behavior. The policy is demonstrated in simulation to be more robust to novel
observations and take safer actions than an uncertainty-unaware baseline.
