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.

翻译：理解自动驾驶车辆与人工驾驶车辆之间的相互依赖性仍然是一个持续的挑战，这对自动驾驶的安全性和可行性具有重要影响。这种相互依赖性源于道路使用者之间固有的交互作用。因此，自动驾驶车辆（AVs）必须能够理解并分析人类驾驶车辆的意图，并展现出其他交通参与者能够理解的行为。为此，本文提出了GTP-UDRIVE，一个考虑混合交通环境的统一博弈论轨迹规划与决策器。我们的模型在决策过程中考虑了其他车辆的意图，并基于回旋曲线插值技术为自动驾驶车辆提供类人轨迹。本研究探讨了一种基于粒子群优化（PSO）的求解器，该求解器能快速收敛至最优决策。在高度交互的交通场景中，交叉路口通行尤其具有挑战性。因此，我们选择在无信号交叉路口，利用实验性自动驾驶车辆在真实交通条件下验证我们方法的可行性和有效性。测试结果表明，我们的方法适用于：1）同时进行决策和生成轨迹；2）将车辆轨迹描述为分段回旋曲线并强制执行几何约束；3）降低轨迹优化问题的搜索空间维度。