Autonomous vehicles (AVs) enhance driving efficiency and reduce accidents but require robust risk assessment methods due to dense traffic and uncertainties. Existing methods rely on predefined rules, which lack generalization. This paper presents a novel risk quantification method without expert rules, leveraging reinforcement learning and adversarial agents. The proposed model uses Gated Transformer Networks for multivariate time series regression, analyzing historical traffic data to generate continuous risk assessments. Simulation experiments validate the method's efficacy, demonstrating its precision and robustness.
A Data-Driven Risk Assessment Method for Autonomous Vehicles Without Expert Rule Design
24.09.2024
1245304 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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