Intention-aware Policy Graphs, applied to 830 nuScenes driving scenes, attribute desires and intentions to an autonomous vehicle and produce interpretable global and local teleological explanations, with a reported 75.7% interpretability and 92.0% reliability.
Apply- ing and Verifying an Explainability Method Based on Policy Graphs in the Context of Reinforcement Learning
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Explaining Autonomous Vehicles with Intention-aware Policy Graphs
Intention-aware Policy Graphs, applied to 830 nuScenes driving scenes, attribute desires and intentions to an autonomous vehicle and produce interpretable global and local teleological explanations, with a reported 75.7% interpretability and 92.0% reliability.