Takeover performance prediction model considering cognitive load: analysis of subjective and objective factors.

Abstract

Objectives: This paper aims to explore the effects of different cognitive loads on driver eye movement and ECG, and construct the BP (Back Propagation) neural network prediction model of driver takeover performance optimized by genetic algorithm (GA).

Methods: In this paper, the simulation software UC-win/road was used to construct the highway driving scene, and the N-back tasks of different difficulty were selected to set different levels of cognitive load for testing. Using the driver eye movement data and ECG data collected during the test, combined with the NASA-TLX load scale collected after the driving simulation test, the subjective and objective data were analyzed. We determined the cognitive load level of drivers under different cognitive tasks based on the K-means clustering algorithm. We selected the significant objective indicators that affect the cognitive load of drivers, constructed a takeover performance prediction model based on BP neural network, and verified the effectiveness.

Results: Compared with the BP prediction model, the GA-BP prediction model established in this paper has different degrees of improvement in each evaluation index under different time window lengths. Among them, the improvement effect is the most obvious under the length of 10s time window, the accuracy rate is increased by 5.51%, the recall rate is increased by 7.08%, the accuracy rate is increased by 6.19%, and the F1 score is increased by 9.71%.

Conclusions: The findings indicate that as the difficulty of the cognitive sub-task escalates, the driver's tension increases and the cognitive load increases. The GA-BP prediction model established in this paper has higher prediction accuracy.