Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters

TL;DR

This paper proposes a new model to improve the robustness of autonomous steering control against camera angle perturbations caused by pothole encounters, reducing steering prediction errors significantly.

Contribution

The paper introduces a novel model specifically designed to compensate for camera angle perturbations during pothole encounters in autonomous driving systems.

Findings

Reduces steering angle prediction errors to 2.3% under perturbations

Demonstrates robustness of the model on publicly available datasets

Addresses a previously underexplored issue in autonomous navigation

Abstract

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.