Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

翻译：连接技术通过提供超出单车感知与预测能力的信息，在提升交通系统安全性与效率方面展现出巨大潜力。然而，在完全实现网联化自动化交通系统的过渡期内，人类驾驶车辆与自动驾驶车辆、网联车辆与非网联车辆预计将共享交通网络。这种混合交通场景显著增加了分析系统行为和量化高度交互场景（如换道）不确定性的复杂性。当采用基于神经网络的规划器进一步提升效率时，确保系统安全性更加困难。本文提出一种连接增强型神经网络换道规划器。通过与动态环境中周围网联车辆协同配合，该规划器将根据安全规避轨迹的分析结果自适应调整其规划轨迹。通过大量仿真实验，我们验证了所提规划器在多种混合交通场景中提升效率与保障安全性的优势，并分析了通信或协调非理想状态下的系统鲁棒性。