  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.
