Abstract
The automation of road intersections is increasingly considered as an inevitable next step toward a higher level of autonomy on our roads. For the particular case of fully automated vehicles, we propose a distributed model predictive control approach in which multiple agents are able to pass the intersection simultaneously while keeping a sufficient safety distance to conflicting agents. Therefore, each agent solves a local optimization problem subject to non-convex safety constraints which couple the agents. In order to handle these coupling constraints, we propose constraint prioritization. With that methodology, for two pairwise conflicting agents, the safety constraint is only imposed on the agent with lower priority which does not imply any a priori intersection passing order. Finally, we can solve the distributed optimization problem in parallel without any nested iterations. To solve the local non-convex optimization problems, we apply a semidefinite programming relaxation in combination with randomization to obtain appropriate and feasible solutions. A simulation study finally proves the efficacy of our approach.
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Notes
- 1.
This article extends the previous publication [20] published by the International Federation of Automatic Control (IFAC).
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Acknowledgements
This contribution extends the previous publication [20] which is under copyright of the International Federation of Automatic Control (IFAC), 2017. Parts of this contribution (modeling, description, and implementation of the control and optimization problem) have been reused with the permission of IFAC which is acknowledged with high appreciation. This chapter, though, provides a more comprehensive overview of the applied algorithms and an extensive analysis of simulation results in a different intersection scenario.
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Katriniok, A., Kleibaum, P., Joševski, M. (2019). Automation of Road Intersections Using Distributed Model Predictive Control. In: Waschl, H., Kolmanovsky, I., Willems, F. (eds) Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions . Lecture Notes in Control and Information Sciences, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-319-91569-2_9
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