Banaie, Fatemeh (University of Huddersfield), Babaei, Esmaeil (Manchester Metropolitan University), Parkinson, Simon (University of Huddersfield), Vallati, Mauro (University of Huddersfield)

Strategic Decision-Making under Deception at Intersections: A Bayesian Game Approach

Scheduled for presentation during the Invited Session "S24a-Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond" (TH-LM-T24), Thursday, November 20, 2025, 10:30−10:50, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Connected Autonomous Vehicles (CAVs) offer substantial potential to improve urban mobility by enabling safer, more efficient, and environmentally sustainable transportation. However, deceptive behaviours--such as transmitting false information or acting unpredictably--pose significant risks to safety and system reliability, particularly at intersections where rapid decisions must be made under uncertainty. Unlike existing work that treats deception solely as a security threat, this paper models deception as a strategic choice using a Bayesian game-theoretic framework. Our approach introduces independent additive penalties for efficiency, comfort, and safety that adjust based on the probability of deception and relative vehicle positions, thus balancing strategic interactions between leading and lagging vehicles. The framework is evaluated via simulations under fixed, adaptive, and Nash equilibrium-based yielding scenarios. Results demonstrate that equilibrium-driven strategies significantly mitigate the adverse effects of deception while enhancing fairness and overall utility in connected autonomous traffic systems.