The role of machine learning for trajectory prediction in cooperative driving

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. CCS CONCEPTS • Computing methodologies → Reinforcement learning; Machine learning; • Networks → Cloud computing.

Proposing Cooperative Maneuvers Among Automated Vehicles Using Machine Learning

“…Chandramohan et al [5] use Deep Q-Learning to enable an automated vehicle to avoid collisions on a multi-lane highway. Sequeira and Mahmoodi [17] develop a traffic orchestrator proposing trajectories for connected vehicles. Our approach adds to this discussion by using ML to propose good cooperative overtake maneuvers.…”

“…Finding driving strategies often involves machine learning (ML) [5,15,17,18]. We investigate whether ML can also help make decisions on cooperative maneuvers.…”