A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications

TL;DR

This paper introduces a new real-world urban driving dataset focused on maneuvers, along with a model for classifying and predicting driving behaviors, supporting cooperative vehicle applications in mixed traffic environments.

Contribution

It provides a novel maneuver-based urban driving dataset and a predictive model that captures maneuver-specific patterns for automated and cooperative driving systems.

Findings

The dataset captures diverse urban driving maneuvers.

The model effectively classifies maneuver types.

Results support improved prediction in mixed traffic scenarios.

Abstract

Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on driving data of the human driver. In this work, we investigate the currently available driving datasets and introduce a real-world maneuver-based driving dataset that is collected during our urban driving data collection campaign. We also provide a model that embeds the patterns in maneuver-specific samples. Such model can be employed for classification and prediction purposes.