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Artificial Intelligence and Digital Twins play an integral role in drivinginnovation in the domain of intelligent driving. Long short-term memory (LSTM)is a leading driver in the field of lane change prediction for manoeuvreanticipation. However, the decision-making process of such models is complexand non-transparent, hence reducing the trustworthiness of the smart solution.This work presents an innovative approach and a technical implementation forexplaining lane change predictions of layer normalized LSTMs using Layer-wiseRelevance Propagation (LRP). The core implementation includes consuming livedata from a digital twin on a German highway, live predictions and explanationsof lane changes by extending LRP to layer normalized LSTMs, and an interfacefor communicating and explaining the predictions to a human user. We aim todemonstrate faithful, understandable, and adaptable explanations of lane changeprediction to increase the adoption and trustworthiness of AI systems thatinvolve humans. Our research also emphases that explainability andstate-of-the-art performance of ML models for manoeuvre anticipation go hand inhand without negatively affecting predictive effectiveness.

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation