  Guaranteeing real-time and accurate object detection simultaneously is
paramount in autonomous driving environments. However, the existing object
detection neural network systems are characterized by a tradeoff between
computation time and accuracy, making it essential to optimize such a tradeoff.
Fortunately, in many autonomous driving environments, images come in a
continuous form, providing an opportunity to use optical flow. In this paper,
we improve the performance of an object detection neural network utilizing
optical flow estimation. In addition, we propose a Lyapunov optimization
framework for time-average performance maximization subject to stability. It
adaptively determines whether to use optical flow to suit the dynamic vehicle
environment, thereby ensuring the vehicle's queue stability and the
time-average maximum performance simultaneously. To verify the key ideas, we
conduct numerical experiments with various object detection neural networks and
optical flow estimation networks. In addition, we demonstrate the
self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which
are the real-time object detection network and an optical flow estimation
network, respectively. In the demonstration, our proposed framework improves
the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue
stability for computing capabilities.
