# Robust Lane Tracking with Multi-mode Observation Model and Particle Filtering

## TL;DR

This paper introduces a particle filter-based approach with a multi-mode observation model for robust lane tracking, outperforming traditional Kalman Filter methods in handling non-Gaussian observation data.

## Contribution

The paper presents a novel multi-mode observation model combined with particle filtering to improve lane tracking accuracy over existing Kalman Filter approaches.

## Key findings

- Particle filter with multi-mode observation model outperforms Kalman Filter in lane tracking.
- The proposed method handles non-Gaussian observations more effectively.
- Experimental results demonstrate superior robustness and accuracy in various scenarios.

## Abstract

Automatic lane tracking involves estimating the underlying signal from a sequence of noisy signal observations. Many models and methods have been proposed for lane tracking, and dynamic targets tracking in general. The Kalman Filter is a widely used method that works well on linear Gaussian models. But this paper shows that Kalman Filter is not suitable for lane tracking, because its Gaussian observation model cannot faithfully represent the procured observations. We propose using a Particle Filter on top of a novel multiple mode observation model. Experiments show that our method produces superior performance to a conventional Kalman Filter.