# Path Assignment Techniques For Vehicle Tracking

## TL;DR

This paper reviews vehicle path estimation techniques and introduces two new methods that utilize posterior probability distributions and median estimators to improve lane assignment accuracy in driver assistance systems.

## Contribution

The paper proposes two novel path assignment methods that delay filtering to reduce artifacts and improve lane detection accuracy in vehicle tracking.

## Key findings

- Methods outperform traditional approaches in ROC tests
- Filtering later in processing reduces delays and artifacts
- Experimental data validates the effectiveness of proposed techniques

## Abstract

Many driver assistance systems such as Adaptive Cruise Control require the identification of the closest vehicle that is in the host vehicle's path. This entails an assignment of detected vehicles to the host vehicle path or neighboring paths. After reviewing approaches to the estimation of the host vehicle path and lane assignment techniques we introduce two methods that are motivated by the rationale to filter measured data as late in the processing stages as possible in order to avoid delays and other artifacts of intermediate filters. These filters generate discrete posterior probability distributions from which a path or "lane" index is extracted by a median estimator. The relative performance of those methods is illustrated by a ROC using experimental data and labeled ground truth data.