Adversarial Risk Analysis for Automated Lane-Changing in Heterogeneous Traffic
Document Type
Book
Publication Date
10-16-2024
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. Abstract © SpringerNature
Source Publication
Algorithmic Decision Theory (ISBN 978-3-031-73903-3), LNAI 15248
Recommended Citation
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 (LNAI), vol 15248. Springer, Cham. https://doi.org/10.1007/978-3-031-73903-3_9
Comments
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland
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