Abstract
The global transition from manned to automated vehicles is anticipated to occur incrementally. As such, interactions between automated driving systems (ADS) and manned vehicles motivate related decision-support research. This manuscript develops a novel modeling framework based on adversarial risk analysis focusing on lane-changing maneuvers. An empirical evaluation is provided within a simulated environment serving to validate the modeling approach and solution methodology under a specified traffic scene. Additional model extensions to alternative traffic scenes and different driver-rationality assumptions are provided. In so doing, we showcase the potential for decision theory to manage ADS behavior in heterogeneous traffic. This research also highlights the need for an ADS to prudently balance computational resources between perception and decision tasks.
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Notes
- 1.
\(\mathbb {E}_A\) indicates the expectation is taken over quantities unknown to the ADS.
- 2.
Code is available at https://github.com/roinaveiro/ads_lane_changing.
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Acknowledgments/Disclaimer
Research supported by EU’s Horizon 2020 project 815003 Trustonomy, the AMALFI FBBVA project, the Air Force Scientific Office of Research (AFOSR) award FA-9550-21-1-0239, and AFOSR European Office of Aerospace Research and Development award FA8655-21-1-7042. DRI is supported by the AXA-ICMAT Chair and the Spanish Ministry of Science program PID2021-124662OB-I00. Views expressed do not reflect the official position of the US Government; cleared for public release by the USAFA PA office.
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Naveiro, R., Ríos Insua, D., Caballero, W.N. (2025). Adversarial Risk Analysis for Automated Lane-Changing in Heterogeneous Traffic. In: Freeman, R., Mattei, N. (eds) Algorithmic Decision Theory. ADT 2024. Lecture Notes in Computer Science(), vol 15248. Springer, Cham. https://doi.org/10.1007/978-3-031-73903-3_9
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