CaSTFormer: Causal Spatio-Temporal Transformer for Driving Intention Prediction
This addresses the challenge of modeling complex spatio-temporal interdependencies and unpredictable human driving behavior for autonomous driving safety and efficiency, representing a novel method for a known bottleneck.
The paper tackled the problem of accurately predicting driving intention in human-machine co-driving systems by proposing CaSTFormer, a Causal Spatio-Temporal Transformer, which achieved state-of-the-art performance on the Brain4Cars dataset.
Accurate prediction of driving intention is key to enhancing the safety and interactive efficiency of human-machine co-driving systems. It serves as a cornerstone for achieving high-level autonomous driving. However, current approaches remain inadequate for accurately modeling the complex spatio-temporal interdependencies and the unpredictable variability of human driving behavior. To address these challenges, we propose CaSTFormer, a Causal Spatio-Temporal Transformer to explicitly model causal interactions between driver behavior and environmental context for robust intention prediction. Specifically, CaSTFormer introduces a novel Reciprocal Shift Fusion (RSF) mechanism for precise temporal alignment of internal and external feature streams, a Causal Pattern Extraction (CPE) module that systematically eliminates spurious correlations to reveal authentic causal dependencies, and an innovative Feature Synthesis Network (FSN) that adaptively synthesizes these purified representations into coherent spatio-temporal inferences. We evaluate the proposed CaSTFormer on the public Brain4Cars dataset, and it achieves state-of-the-art performance. It effectively captures complex causal spatio-temporal dependencies and enhances both the accuracy and transparency of driving intention prediction.