Motion prediction is a key factor towards the full deployment of autonomous vehicles. It is fundamental in order to ensure safety while navigating through highly interactive and complex scenarios. Lack of visibility due to an obstructed view or sensor range poses a great safety issue for autonomous vehicles. The inclusion of occlusion in interaction-aware approaches is not very well explored in the literature. In this work, the MultIAMP framework, which produces multimodal probabilistic outputs from the integration of a Dynamic Bayesian Network and Markov chains, is extended to tackle occlusions. The framework is evaluated with a state-of-the-art motion planner in two realistic use cases.

翻译：运动预测是实现自动驾驶车辆全面部署的关键因素，对于确保在高度交互和复杂场景中安全导航至关重要。由于视野受阻或传感器范围限制导致的能见度不足，对自动驾驶车辆构成了重大安全隐患。在交互感知方法中纳入遮挡因素的研究在现有文献中尚未得到充分探索。本研究扩展了MultIAMP框架，该框架通过整合动态贝叶斯网络和马尔可夫链生成多模态概率输出，以处理遮挡问题。该框架在两个现实用例中与最先进的运动规划器进行了评估。