In this work, we present a reward-driven automated curriculum reinforcement learning approach for interaction-aware self-driving at unsignalized intersections, taking into account the uncertainties associated with surrounding vehicles (SVs). These uncertainties encompass the uncertainty of SVs' driving intention and also the quantity of SVs. To deal with this problem, the curriculum set is specifically designed to accommodate a progressively increasing number of SVs. By implementing an automated curriculum selection mechanism, the importance weights are rationally allocated across various curricula, thereby facilitating improved sample efficiency and training outcomes. Furthermore, the reward function is meticulously designed to guide the agent towards effective policy exploration. Thus the proposed framework could proactively address the above uncertainties at unsignalized intersections by employing the automated curriculum learning technique that progressively increases task difficulty, and this ensures safe self-driving through effective interaction with SVs. Comparative experiments are conducted in $Highway\_Env$, and the results indicate that our approach achieves the highest task success rate, attains strong robustness to initialization parameters of the curriculum selection module, and exhibits superior adaptability to diverse situational configurations at unsignalized intersections. Furthermore, the effectiveness of the proposed method is validated using the high-fidelity CARLA simulator.

翻译：在本文中，我们提出了一种奖励驱动的自动课程强化学习方法，用于无信号交叉口的交互感知自动驾驶，该方法考虑了周围车辆（SVs）相关的不确定性。这些不确定性包括SVs驾驶意图的不确定性以及SVs数量的不确定性。为解决该问题，课程集被专门设计为容纳逐渐增多的SVs数量。通过实施自动课程选择机制，重要性权重在不同课程间得到合理分配，从而提升了样本效率和训练效果。此外，奖励函数被精心设计以引导智能体进行有效的策略探索。因此，所提出的框架能够通过采用任务难度逐步增加的自动课程学习技术，主动应对无信号交叉口的上述不确定性，并通过与SVs的有效交互确保安全自动驾驶。在$Highway\_Env$中进行了对比实验，结果表明，我们的方法取得了最高的任务成功率，对课程选择模块的初始化参数具有强鲁棒性，并在无信号交叉口的多种情景配置下展现出优越的适应性。此外，通过高保真度CARLA模拟器验证了所提方法的有效性。