An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems

TL;DR

This paper introduces an advanced motion planning method for automated driving systems that incorporates driving heterogeneity and long-term trajectory prediction to enhance safety in highway merging scenarios.

Contribution

It presents a novel hierarchical model that integrates driving behavior and long-term trajectory predictions of surrounding vehicles for improved motion planning.

Findings

Enhanced safety in highway merging scenarios

Effective integration of behavior and trajectory predictions

Improved motion planning performance

Abstract

Navigating automated driving systems (ADSs) through complex driving environments is difficult. Predicting the driving behavior of surrounding human-driven vehicles (HDVs) is a critical component of an ADS. This paper proposes an enhanced motion-planning approach for an ADS in a highway-merging scenario. The proposed enhanced approach utilizes the results of two aspects: the driving behavior and long-term trajectory of surrounding HDVs, which are coupled using a hierarchical model that is used for the motion planning of an ADS to improve driving safety.