  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.
