A Dual-Encoder framework for Enhancing Driving Behavior and Mission Profiling Characterization

Capturing the intricate dynamics of complex systems often requires integrating time-series data from multiple sources. However, effectively unifying and analyzing these heterogeneous streams poses significant methodological challenges. This work proposes a contrastive dual-encoder framework that learns a shared latent representation across distinct timeseries modalities. By mapping semantically related segments of different data views to proximate regions in the latent space, our approach captures cross-modal dependencies in a manner that traditional single-view models often overlook. We illustrate the framework in a motorcycle riding application, where rider behavior signals (e.g., gear, throttle, braking) are embedded jointly with vehicle dynamics (e.g., speed, lean angle, acceleration). We used real-world data to demonstrate the framework’s utility in uncovering how rider inputs translate into specific vehicle states and vice versa. Beyond validating cross-modal alignment, the learned embeddings facilitate robust downstream analyses, including feature importance via SHAP, rider profiling based on latent-space clustering, and mission profile characterization that links riding objectives to observed patterns. These findings underscore the effectiveness of dual-encoder architectures in enhancing the analysis and interpretation of complex multimodal time-series data.