An enhanced motion planning approach by integrating driving heterogeneity and long-term trajectory prediction for automated driving systems
This addresses safety challenges for automated driving systems in highway merging, but appears incremental as it builds on existing motion-planning methods.
The paper tackles the problem of navigating automated driving systems in complex highway-merging scenarios by integrating driving heterogeneity and long-term trajectory prediction of human-driven vehicles, resulting in improved driving safety.
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.