A Framework for Predicting Future System Performance in Autonomous Unmanned Ground Vehicles
针对自主军事地面车辆在复杂越野环境中的性能预测问题，提出基于环境复杂度和学习成本的框架，利用DARPA实验数据验证了预测准确性。
The development of complex self-adaptive systems has accelerated rapidly over the past decade, led by the Department of Defense, which has sought to develop and field military systems, such as unmanned aerial vehicles and unmanned ground vehicles, with elevated levels of autonomy to accomplish their mission with reduced funding and manpower. As their role increases, such systems must be able to adapt and learn, and make nondeterministic decisions. To field such systems requires extensive testing, evaluation, verification, and validation-a challenging task. To address this, we apply a novel systems perspective to develop a framework to predict future system performance based on the complexity of the operating environment using newly introduced complexity measures and learned costs. In this paper, we consider an autonomous military ground robot navigating in complex off-road environments. Using our model and experimental data from Defense Advanced Research Projects Agency-led experiments, we demonstrate the accuracy with which our model can predict system performance and then validate our model against other experimental results.