Active safety systems on vehicles often face problems with false alarms. Most active safety systems predict the driver's trajectory with the assumption that the driver is always in a normal emotion, and then infer risks. However, the driver's trajectory uncertainty increases under abnormal emotions. This paper proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle trajectories under abnormal emotions. At the physical level, the interaction features between vehicles are extracted by the physical GCN module. At the cognitive level, SOR cognitive theory is used as prior knowledge to build a Dynamic Bayesian Network (DBN) structure. The conditional probability and state transition probability of nodes from the calibrated SOR-DBN quantify the causal relationship between cognitive factors, which is embedded into the cognitive GCN module to extract the characteristics of the influence mechanism of emotions on driving behavior. The CARLA-SUMO joint driving simulation platform was built to develop dangerous pre-crash scenarios. Methods of recreating traffic scenes were used to naturally induce abnormal emotions. The experiment collected data from 26 participants to verify the proposed model. Compared with the model that only considers physical motion features, the prediction accuracy of the proposed model is increased by 68.70%. Furthermore,considering the SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with other advanced trajectory prediction models, the results of CPSOR-GCN also have lower errors. This model can be integrated into active safety systems to better adapt to the driver's emotions, which could effectively reduce false alarms.

翻译：车辆主动安全系统常面临误报警问题。大多数主动安全系统在假设驾驶员始终处于正常情绪状态下预测其轨迹，进而推断风险。然而，在异常情绪下，驾驶员的轨迹不确定性会增加。本文提出一种新的轨迹预测模型：CPSOR-GCN，用于预测异常情绪下的车辆轨迹。在物理层面，通过物理GCN模块提取车辆间的交互特征。在认知层面，采用SOR认知理论作为先验知识构建动态贝叶斯网络（DBN）结构。基于校准的SOR-DBN中节点的条件概率与状态转移概率，量化认知因素间的因果关系，并将其嵌入认知GCN模块，以提取情绪对驾驶行为影响机制的特征。搭建了CARLA-SUMO联合驾驶仿真平台，用于开发危险碰撞前场景。采用交通场景复现方法自然诱发异常情绪。实验收集了26名参与者的数据对所提模型进行验证。与仅考虑物理运动特征的模型相比，所提模型的预测精度提升了68.70%。此外，考虑SOR-DBN使轨迹预测误差降低了15.93%。与其他先进轨迹预测模型相比，CPSOR-GCN的结果同样具有更低的误差。该模型可集成于主动安全系统中，以更好地适应驾驶员情绪，从而有效减少误报警。