In the rapidly evolving field of autonomous driving, reliable prediction is pivotal for vehicular safety. However, trajectory predictions often deviate from actual paths, particularly in complex and challenging environments, leading to significant errors. To address this issue, our study introduces a novel method for Dynamic Occupancy Set (DOS) prediction, it effectively combines advanced trajectory prediction networks with a DOS prediction module, overcoming the shortcomings of existing models. It provides a comprehensive and adaptable framework for predicting the potential occupancy sets of traffic participants. The innovative contributions of this study include the development of a novel DOS prediction model specifically tailored for navigating complex scenarios, the introduction of precise DOS mathematical representations, and the formulation of optimized loss functions that collectively advance the safety and efficiency of autonomous systems. Through rigorous validation, our method demonstrates marked improvements over traditional models, establishing a new benchmark for safety and operational efficiency in intelligent transportation systems.

翻译：在快速发展的自动驾驶领域，可靠的预测对车辆安全至关重要。然而，轨迹预测常常偏离实际路径，尤其是在复杂和具有挑战性的环境中，导致显著误差。为解决这一问题，本研究提出了一种用于动态占据集预测的新方法，它有效地将先进的轨迹预测网络与DOS预测模块相结合，克服了现有模型的不足。该方法为预测交通参与者的潜在占据集提供了一个全面且适应性强的框架。本研究的创新贡献包括：开发了一种专门为应对复杂场景而设计的新型DOS预测模型，引入了精确的DOS数学表示，并制定了优化的损失函数，这些共同提升了自动驾驶系统的安全性和效率。通过严格验证，我们的方法相比传统模型展现出显著改进，为智能交通系统的安全性和运行效率设立了新基准。