Deep neural network controllers for autonomous driving have recently
benefited from significant performance improvements, and have begun deployment
in the real world. Prior to their widespread adoption, safety guarantees are
needed on the controller behaviour that properly take account of the
uncertainty within the model as well as sensor noise. Bayesian neural networks,
which assume a prior over the weights, have been shown capable of producing
such uncertainty measures, but properties surrounding their safety have not yet
been quantified for use in autonomous driving scenarios. In this paper, we
develop a framework based on a state-of-the-art simulator for evaluating
end-to-end Bayesian controllers. In addition to computing pointwise uncertainty
measures that can be computed in real time and with statistical guarantees, we
also provide a method for estimating the probability that, given a scenario,
the controller keeps the car safe within a finite horizon. We experimentally
evaluate the quality of uncertainty computation by several Bayesian inference
methods in different scenarios and show how the uncertainty measures can be
combined and calibrated for use in collision avoidance. Our results suggest
that uncertainty estimates can greatly aid decision making in autonomous
driving.

本论文提出了一种基于最先进的模拟器的框架，以评估端到端 Bayesian 控制器，实验评估了不同场景下用于避碰的不确定性计算的质量并表明不确定性估计可大大帮助自动驾驶车辆的决策。