Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to assure safety while navigating through highly interactive complex scenarios. In this work, the framework IAMP (Interaction- Aware Motion Prediction), producing multi-modal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov Chains, is extended with a learning-based approach. The integration of a machine learning model tackles the limitations of the ruled-based mechanism since it can better adapt to different driving styles and driving situations. The method here introduced generates context-dependent acceleration distributions used in a Markov-chain-based motion prediction. This hybrid approach results in better evaluation metrics when compared with the baseline in the four

翻译：摘要：运动预测是实现自动驾驶汽车全面部署的关键因素。在充满高度交互的复杂场景中行驶时，确保安全性至关重要。本文对IAMP（交互感知运动预测）框架进行了扩展，该框架通过结合动态贝叶斯网络与马尔可夫链生成多模态概率输出，并引入了一种基于学习的方法。机器学习模型的集成解决了基于规则机制的局限性，因为它能更好地适应不同的驾驶风格与驾驶情境。本文提出的方法生成了依赖于上下文的加速度分布，并应用于基于马尔可夫链的运动预测。与基线方法在四个测试场景中的对比结果表明，这种混合方法在评估指标上表现更优。