Trajectory forecast for cyclists in cooperative interactions with automated vehicles

Abstract

In shared traffic spaces like intersections cooperative behavior can be crucial for safe and comfortable interactions of traffic participants. In mixed urban traffic, VRUs like cyclists need special attention in interactions with autonomous vehicles. The goal of this work is to provide the automated vehicle with trajectory information of the cyclist, to be able to take the behavioral intention of the cyclist into account and make a cooperative reaction possible. This is achieved through a trajectory forecast of the cyclist, which allows for the possibility to estimate his course of movement within a limited time frame. Multiple algorithms for a trajectory forecast have been implemented, compared and evaluated. The results of this research work showed that a CNN can be used to integrate data of various types in order to accomplish a trajectory forecast for cyclists.

Keywords: Trajectory Forecast, Autonomous Driving, Machine-Learning, Neural Networks, Cooperative Interaction

DOI: 10.54941/ahfe1002781