Predicting potential collision events is beneficial to ensure the driving safety of autonomous vehicles. Existing graph-based collision prediction methods rely heavily on domain knowledge and predefined semantic relations, limiting their flexibility and adaptability in complex driving scenarios. To overcome these challenges, this paper introduces a novel collision prediction framework named HGAT-CP, which integrates a Heterogeneous Graph Attention Network (HGAT) with a Long Short-Term Memory network (LSTM) to model the spatial-temporal interactions in scenes. First, the proposed method employs a data-driven scene graph embedding module to autonomously learn relationships between vehicles and lanes and construct flexible scene graphs. Then, the HGAT module utilizes a dual-level attention mechanism, operating at both the node level and type level, to capture spatial interactions without relying on predefined semantic rules. The LSTM module models temporal dependencies of the scene graph embeddings to improve the prediction of collision events over time. Experimental evaluations on public datasets demonstrate that our proposed method achieves state-of-the-art performance, outperforming existing methods across all metrics.