Vehicle manufacturers are racing to create autonomous navigation and steering control algorithms for their vehicles. These software are made to handle various real-life scenarios such as obstacle avoidance and lane maneuvering. There is some ongoing research to incorporate pothole avoidance into these autonomous systems. However, there is very little research on the effect of hitting a pothole on the autonomous navigation software that uses cameras to make driving decisions. Perturbations in the camera angle when hitting a pothole can cause errors in the predicted steering angle. In this paper, we present a new model to compensate for such angle perturbations and reduce any errors in steering control prediction algorithms. We evaluate our model on perturbations of publicly available datasets and show our model can reduce the errors in the estimated steering angle from perturbed images to 2.3%, making autonomous steering control robust against the dash cam image angle perturbations induced when one wheel of a car goes over a pothole.

翻译：汽车制造商正竞相为其车辆开发自主导航和转向控制算法。这些软件旨在处理各种现实场景，如避障和车道操控。目前已有部分研究将坑洞规避功能整合到自主系统中，但关于车辆碾压坑洞对基于摄像头图像决策的自主导航软件影响的研究却十分有限。当车辆驶过坑洞时，摄像头角度的扰动会导致预测转向角产生误差。本文提出了一种新型模型来补偿此类角度扰动，并减少转向控制预测算法中的误差。我们在公开数据集的扰动图像上评估了该模型，结果表明，该模型能将扰动图像中估算转向角的误差降低至2.3%，从而使自主转向控制对单轮驶过坑洞所引发的行车记录仪图像角度扰动具有鲁棒性。