论文标题
具有标准化LSTM的数字双胞胎上的可解释在线车道变更预测和层面相关性传播
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation
论文作者
论文摘要
人工智能和数字双胞胎在驱动智能驾驶领域的创新方面起着不可或缺的作用。长期记忆（LSTM）是车道变更预测领域的领先驱动力。但是，这种模型的决策过程是复杂且非透明的，因此降低了智能解决方案的可信度。这项工作提出了一种创新的方法和技术实施，用于使用层的相关性传播（LRP）来解释层归一化LSTM的车道变更预测。核心实现包括通过将LRP扩展到标准化的LSTM来消费来自德国高速公路上的数字双胞胎的实时数据，对车道变化的解释，以及向人用户传达和解释预测的界面。我们旨在证明对车道变更预测的忠实，易于理解和适应性的解释，以增加涉及人类的AI系统的采用和可信赖性。我们的研究还强调，ML模型进行操作预期的解释性和最先进的性能是齐头并进的，而不会对预测效率产生负面影响。
Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the field of lane change prediction for manoeuvre anticipation. However, the decision-making process of such models is complex and non-transparent, hence reducing the trustworthiness of the smart solution. This work presents an innovative approach and a technical implementation for explaining lane change predictions of layer normalized LSTMs using Layer-wise Relevance Propagation (LRP). The core implementation includes consuming live data from a digital twin on a German highway, live predictions and explanations of lane changes by extending LRP to layer normalized LSTMs, and an interface for communicating and explaining the predictions to a human user. We aim to demonstrate faithful, understandable, and adaptable explanations of lane change prediction to increase the adoption and trustworthiness of AI systems that involve humans. Our research also emphases that explainability and state-of-the-art performance of ML models for manoeuvre anticipation go hand in hand without negatively affecting predictive effectiveness.
