  Understanding the interdependence between autonomous and human-operated
vehicles remains an ongoing challenge, with significant implications for the
safety and feasibility of autonomous driving.This interdependence arises from
inherent interactions among road users.Thus, it is crucial for Autonomous
Vehicles (AVs) to understand and analyze the intentions of human-driven
vehicles, and to display behavior comprehensible to other traffic
participants.To this end, this paper presents GTP-UDRIVE, a unified
game-theoretic trajectory planner and decision-maker considering a
mixed-traffic environment. Our model considers the intentions of other vehicles
in the decision-making process and provides the AV with a human-like
trajectory, based on the clothoid interpolation technique.% This study
investigates a solver based on Particle Swarm Optimization (PSO) that quickly
converges to an optimal decision.Among highly interactive traffic scenarios,
the intersection crossing is particularly challenging. Hence, we choose to
demonstrate the feasibility and effectiveness of our method in real traffic
conditions, using an experimental autonomous vehicle at an unsignalized
intersection. Testing results reveal that our approach is suitable for 1)
Making decisions and generating trajectories simultaneously. 2) Describing the
vehicle's trajectory as a piecewise clothoid and enforcing geometric
constraints. 3) Reducing search space dimensionality for the trajectory
optimization problem.
