  Trajectory planning for connected and automated vehicles (CAVs) has the
potential to improve operational efficiency and vehicle fuel economy in traffic
systems. Despite abundant studies in this research area, most of them only
consider trajectory planning in the longitudinal dimension or assume the fully
CAV environment. This study proposes an approach to the decentralized planning
of CAV trajectories at an isolated signalized intersection under the mixed
traffic environment, which consists of connected and human-driven vehicles
(CHVs) and CAVs. A bi-level optimization model is formulated based on discrete
time to optimize the trajectory of a single CAV in both the longitudinal and
lateral dimensions given signal timings and the trajectory information of
surrounding vehicles. The upper-level model optimizes lateral lane-changing
strategies. The lower-level model optimizes longitudinal acceleration profiles
based on the lane-changing strategies from the upper-level model. Minimization
of vehicle delay, fuel consumption, and lane-changing costs are considered in
the objective functions. A Lane-Changing Strategy Tree (LCST) and a Parallel
Monte-Carlo Tree Search (PMCTS) algorithm are designed to solve the bi-level
optimization model. CAV trajectories are planned one by one according to their
distance to the stop bar. A rolling horizon scheme is applied for the dynamic
implementation of the proposed model with time-varying traffic condition.
Numerical studies validate the advantages of the proposed trajectory planning
model compared with the benchmark cases without CAV trajectory planning.
