Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters
This addresses a specific robustness issue in autonomous driving systems for vehicle manufacturers, but it is incremental as it builds on existing pothole avoidance research.
The paper tackles the problem of dash cam image angle perturbations caused by pothole encounters, which can lead to errors in steering control predictions for autonomous navigation systems, and presents a new model that reduces steering angle errors to 2.3%.
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.