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Active safety systems on vehicles often face problems with false alarms. Mostactive safety systems predict the driver's trajectory with the assumption thatthe driver is always in a normal emotion, and then infer risks. However, thedriver's trajectory uncertainty increases under abnormal emotions. This paperproposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicletrajectories under abnormal emotions. At the physical level, the interactionfeatures between vehicles are extracted by the physical GCN module. At thecognitive level, SOR cognitive theory is used as prior knowledge to build aDynamic Bayesian Network (DBN) structure. The conditional probability and statetransition probability of nodes from the calibrated SOR-DBN quantify the causalrelationship between cognitive factors, which is embedded into the cognitiveGCN module to extract the characteristics of the influence mechanism ofemotions on driving behavior. The CARLA-SUMO joint driving simulation platformwas built to develop dangerous pre-crash scenarios. Methods of recreatingtraffic scenes were used to naturally induce abnormal emotions. The experimentcollected data from 26 participants to verify the proposed model. Compared withthe model that only considers physical motion features, the prediction accuracyof the proposed model is increased by 68.70%. Furthermore,considering theSOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared withother advanced trajectory prediction models, the results of CPSOR-GCN also havelower errors. This model can be integrated into active safety systems to betteradapt to the driver's emotions, which could effectively reduce false alarms.

CPSOR-GCN: A Vehicle Trajectory Prediction Method Powered by Emotion and Cognitive Theory