Ensuring safe travels for autonomous vehicles in urban areas are challenging due to the presence of dynamic components and mixed traffic involving both traditional and autonomous vehicles. This study employs a causal probability framework and Bayesian Network (BN) analysis to explore the intricate interactions among various variables contributing to traffic crashes. To achieve this, we utilized real data to assess the impact of time of day and winter conditions on traffic accidents, speed, and the frequency of accidents at specific intersections. Using scene-based methodology, the study categorizes severe weather situations at urban intersections into those with signals and those without. To model variable interactions and support probabilistic reasoning and inference, BNs are employed to depict the interrelationships between variables through conditional probabilities. Finally, the BN model is integrated with the agent-based car learning to act (CARLA) simulator. This integration enhances the modeling of multiple variables and factors, enabling an in-depth analysis of traffic dynamics in various meteorological scenarios. The proposed method facilitates the identification of the composition and interrelationships of elements contributing to traffic accidents. This provides policymakers and traffic engineers with valuable insights for formulating policies and improving road networks to reduce the likelihood of accidents in mixed traffic environments.
Weather Conditions Using Bayesian Networks: A Scene-Based Approach with CARLA Simulator
Lect. Notes in Networks, Syst.
International Congress on Information and Communication Technology ; 2024 ; London, United Kingdom February 19, 2024 - February 22, 2024
Proceedings of Ninth International Congress on Information and Communication Technology ; Kapitel : 32 ; 405-415
27.07.2024
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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