The field of autonomous driving is moving towards increased use of multimodal analysis, combining visual, language, and sensor data to improve driving behavior recognition, risk anticipation, and decision-making. This shift is driven by the need for more accurate and robust models that can handle complex, dynamic environments. Recent research has focused on developing innovative frameworks that integrate multiple data sources, such as video, text, and sensor measurements, to enable more effective and transparent autonomous driving systems. Noteworthy papers include:

• MCAM, which proposes a novel Multimodal Causal Analysis Model for ego-vehicle-level driving video understanding, achieving state-of-the-art performance in visual-language causal relationship learning.
• CAMERA, which introduces a context-aware multi-modal framework for robust accident anticipation, employing an adaptive mechanism guided by scene complexity and gaze entropy to reduce false alarms.
• CMDCL, which proposes a cross-modal dual-causal learning approach for long-term action recognition, introducing a structural causal model to uncover causal relationships between videos and label texts.
• A multimodal framework for explainable autonomous driving, which synergistically combines video, sensor, and textual data to predict driving actions while generating human-readable explanations, fostering trust and regulatory compliance.