Cooperative Decision-Making in Shared Spaces: Making Urban Traffic Safer through Human-Machine Cooperation

TL;DR

This paper introduces a cooperative decision-making framework for automated vehicles that enhances safety and trust in shared spaces by integrating explicit pedestrian prediction models and demonstrating superior real-time performance.

Contribution

It adapts a general cooperative control framework to urban traffic scenarios with pedestrian interaction, incorporating explicit pedestrian prediction for improved decision-making.

Findings

Proposed model outperforms state-of-the-art algorithms in tests.

Framework suitable for real-world applications due to real-time capability.

Enhanced safety and trust in automated vehicle interactions with pedestrians.

Abstract

In this paper, a cooperative decision-making is presented, which is suitable for intention-aware automated vehicle functions. With an increasing number of highly automated and autonomous vehicles on public roads, trust is a very important issue regarding their acceptance in our society. The most challenging scenarios arise at low driving speeds of these highly automated and autonomous vehicles, where interactions with vulnerable road users likely occur. Such interactions must be addressed by the automation of the vehicle. The novelties of this paper are the adaptation of a general cooperative and shared control framework to this novel use case and the application of an explicit prediction model of the pedestrian. An extensive comparison with state-of-the-art algorithms is provided in a simplified test environment. The results show the superiority of the proposed model-based algorithm compared to state-of-the-art solutions and its suitability for real-world applications due to its real-time capability.