Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle

TL;DR

This paper introduces a predictive visual tracking method to detect road surface anomalies like potholes and debris before a vehicle encounters them, functioning effectively even in low visibility or occluded conditions.

Contribution

The novel approach predicts road anomalies using visual tracking of a preceding vehicle, compensating for camera motion and operating reliably in real-time under challenging conditions.

Findings

Detects anomalies reliably at a distance

Operates effectively in low visibility and occlusion

Runs in real-time on standard hardware

Abstract

A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed. The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases. The method operates in low visibility conditions or in dense traffic where the anomaly is occluded by a preceding vehicle. Anomalies are detected predictively, i.e., before a vehicle encounters them, which allows to pre-configure low-level vehicle systems (such as chassis) or to plan an avoidance maneuver in case of autonomous driving. A challenge is that the signal coming from camera-based tracking of a preceding vehicle may be weak and disturbed by camera ego motion due to vibrations affecting the ego vehicle. Therefore, we propose an efficient method to compensate camera pitch rotation by an iterative robust estimator. Our experiments on both controlled setup and normal traffic conditions show that road anomalies can be detected reliably at a distance even in challenging cases where the ego vehicle traverses imperfect road surfaces. The method is effective and performs in real time on standard consumer hardware.