Dilemma zones at signalized intersections present a commonly occurring but unsolved challenge for both drivers and traffic operators. Onsets of the yellow lights prompt varied responses from different drivers: some may brake abruptly, compromising the ride comfort, while others may accelerate, increasing the risk of red-light violations and potential safety hazards. Such diversity in drivers' stop-or-go decisions may result from not only surrounding traffic conditions, but also personalized driving behaviors. To this end, identifying personalized driving behaviors and integrating them into advanced driver assistance systems (ADAS) to mitigate the dilemma zone problem presents an intriguing scientific question. In this study, we employ a game engine-based (i.e., CARLA-enabled) driving simulator to collect high-resolution vehicle trajectories, incoming traffic signal phase and timing information, and stop-or-go decisions from four subject drivers in various scenarios. This approach allows us to analyze personalized driving behaviors in dilemma zones and develop a Personalized Transformer Encoder to predict individual drivers' stop-or-go decisions. The results show that the Personalized Transformer Encoder improves the accuracy of predicting driver decision-making in the dilemma zone by 3.7% to 12.6% compared to the Generic Transformer Encoder, and by 16.8% to 21.6% over the binary logistic regression model.

翻译：信号交叉口的两难区是驾驶员与交通管理者共同面临的常见但尚未解决的挑战。黄灯信号的触发会引发不同驾驶员的差异化反应：有些驾驶员可能急刹车，损害乘坐舒适性；而另一些则可能加速，增加闯红灯的风险与潜在安全隐患。这种停车或通过决策的多样性不仅源于周围交通状况，还可能受到个性化驾驶行为的影响。因此，识别个性化驾驶行为并将其整合至高级驾驶辅助系统（ADAS）中，以缓解两难区问题，是一个引人入胜的科学议题。本研究采用基于游戏引擎的驾驶模拟器（即CARLA）收集高分辨率车辆轨迹、实时交通信号灯相位与时间信息，以及四名受试驾驶员在不同场景下的停车或通过决策。该方法使我们能够分析两难区中的个性化驾驶行为，并开发个性化Transformer编码器以预测个体驾驶员的停车或通过决策。结果显示，相较于通用Transformer编码器，个性化Transformer编码器在两难区驾驶员决策预测准确率上提升3.7%至12.6%，相较于二元逻辑回归模型则提升16.8%至21.6%。