The Role of Machine Learning for Trajectory Prediction in Cooperative Driving

TL;DR

This paper investigates how machine learning can enhance trajectory prediction in cooperative driving scenarios, especially for lane merging, by integrating data from connected vehicles and roadside cameras to improve automated driving safety and efficiency.

Contribution

It introduces a Traffic Orchestrator that combines data from connected vehicles and roadside cameras, applying machine learning for accurate trajectory prediction in cooperative driving.

Findings

Machine learning improves trajectory prediction accuracy.

Integrated data sources enhance cooperative driving safety.

The approach is validated in an automotive test track.

Abstract

In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, cooperative driving is a promising first step in automated driving. The example scenario we explored in this paper, is coordinated lane merge, with data collection, test and evaluation all conducted in an automotive test track. The assumption is that vehicles are a mix of those equipped with communication units on board, i.e. connected vehicles, and those that are not connected. However, roadside cameras are connected and can capture all vehicles including those without connectivity. We develop a Traffic Orchestrator that suggests trajectories based on these two sources of information, i.e. connected vehicles, and connected roadside cameras. Recommended trajectories are built, which are then communicated back to the connected vehicles. We explore the use of different machine learning techniques in accurately and timely prediction of trajectories.