Intelligent systems in autonomous driving increasingly require the ability to infer social intentions to ensure safe and fluid interactions with human road users. However, current approaches typically frame this problem as objective trajectory prediction or fixed classification, ignoring the open-ended nature of human interpretation where a single physical behaviour can generate multiple plausible narratives. To address the gap between trajectory forecasting and narrative understanding, this research investigates how to systematically map the dimensions of variation in driving situations to the range of intention narratives they generate. A literature survey was conducted to distinguish between foundational human social norms and current algorithmic approaches. By integrating script theory with the 3Cs framework (Cues, Characteristics, Classes), this study developed a dimension extraction framework to analyse where objective observations diverge into subjective interpretations. Through comparative analysis of prototypical scenarios (lane merging and pedestrian negotiation), results revealed that current intelligent systems operate predominantly in geometric space, optimizing for physical feasibility, whereas human drivers operate in social space governed by normative scripts. The research concludes that narrative open-endedness is inversely proportional to the strength of physical and social constraints. That is, when constraints are weak, human internal scripts diverge from machine logic, leading to critical prediction errors. Consequently, future systems must shift from raw trajectory output to semantic narrative understanding to explicitly model this uncertainty and align machine reasoning with human expectations. ... Read more