Vehicle Ego-Lane Estimation with Sensor Failure Modeling

TL;DR

This paper introduces a probabilistic ego-lane estimation method using a Hidden Markov Model with failure modeling, improving accuracy and reliability over long highway distances with noisy sensor data.

Contribution

It proposes a novel HMM-based algorithm that incorporates transient failure modeling and leverages map data, enhancing ego-lane estimation accuracy in highway scenarios.

Findings

Achieved stable and reliable ego-lane estimates over 100 km of highway.

Demonstrated effectiveness with different line detectors.

Collected and publicly released annotated datasets for benchmarking.

Abstract

We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The proposed algorithm exploits the OpenStreetMap (or other cartographic services) road property lane number as the expected number of lanes and leverages consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing different line detectors and showing we could achieve much more usable, i.e. stable and reliable, ego-lane estimates over more than 100 Km of highway scenarios, recorded both in Italy and Spain. Moreover, as we could not find a suitable dataset for a quantitative comparison with other approaches, we collected datasets and manually annotated the Ground Truth about the vehicle ego-lane. Such datasets are made publicly available for usage from the scientific community.