  Traffic intersections are important scenes that can be seen almost everywhere
in the traffic system. Currently, most simulation methods perform well at
highways and urban traffic networks. In intersection scenarios, the challenge
lies in the lack of clearly defined lanes, where agents with various motion
plannings converge in the central area from different directions. Traditional
model-based methods are difficult to drive agents to move realistically at
intersections without enough predefined lanes, while data-driven methods often
require a large amount of high-quality input data. Simultaneously, tedious
parameter tuning is inevitable involved to obtain the desired simulation
results. In this paper, we present a novel adaptive and planning-aware
hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios.
Our hybrid-driven method combines an optimization-based data-driven scheme with
a velocity continuity model. It guides the agent's movements using real-world
data and can generate those behaviors not present in the input data. Our
optimization method fully considers velocity continuity, desired speed,
direction guidance, and planning-aware collision avoidance. Agents can perceive
others' motion planning and relative distance to avoid possible collisions. To
preserve the individual flexibility of different agents, the parameters in our
method are automatically adjusted during the simulation. TraInterSim can
generate realistic behaviors of heterogeneous agents in different traffic
intersection scenarios in interactive rates. Through extensive experiments as
well as user studies, we validate the effectiveness and rationality of the
proposed simulation method.
