Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles

TL;DR

This paper introduces a linear driver model that predicts human-like preferred paths for autonomous vehicles based on road curvature, aiming to produce more natural driving behaviors in automated systems.

Contribution

The paper presents a novel linear driver model and an Euler-curve-based fitting algorithm to generate human-preferred paths, improving naturalness in automated lane keeping.

Findings

Model reproduces average human curve path selection behavior

Statistical analysis confirms the validity of the model

Path planning aligns with human driver preferences

Abstract

Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. We propose a linear driver model, which can calculate node points that reflect the preferences of human drivers and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature. We apply this model to a self-developed Euler-curve-based curve fitting algorithm. Through a case study, we show that the model based planned path can reproduce the average behavior of human curve path selection. We analyze the performance of the proposed model through statistical analysis that shows the validity of the captured relations.