  Forecasting trajectories of human-driven vehicles is a crucial problem in
autonomous driving. Trajectory forecasting in the urban area is particularly
hard due to complex interactions with cars and pedestrians, and traffic lights
(TLs). Unlike the former that has been widely studied, the impact of TLs on the
trajectory prediction has been rarely discussed. In this work, we first
identify the less studied, perhaps overlooked impact of TLs. Second, we present
a novel resolution that is mindful of the impact, inspired by the fact that
human drives differently depending on signal phase (green, yellow, red) and
timing (elapsed time). Central to the proposed approach is Human Policy Models
which model how drivers react to various states of TLs by mapping a sequence of
states of vehicles and TLs to a subsequent action (acceleration) of the
vehicle. We then combine the Human Policy Models with a known transition
function (system dynamics) to conduct a sequential prediction; thus our
approach is viewed as Behavior Cloning. One novelty of our approach is the use
of vehicle-to-infrastructure communications to obtain the future states of TLs.
We demonstrate the impact of TL and the proposed approach using an ablation
study for longitudinal trajectory forecasting tasks on real-world driving data
recorded near a signalized intersection. Finally, we propose probabilistic
(generative) Human Policy Models which provide probabilistic contexts and
capture competing policies, e.g., pass or stop in the yellow-light dilemma
zone.
