Driver's turning intent recognition model based on brain activation and contextual information

Alexander Trende; Anirudh Unni; Mischa Jablonski; Bianca Biebl; Andreas Lüdtke; Martin Fränzle; Jochem W Rieger

doi:10.3389/fnrgo.2022.956863

Driver's turning intent recognition model based on brain activation and contextual information

Front Neuroergon. 2022 Aug 16:3:956863. doi: 10.3389/fnrgo.2022.956863. eCollection 2022.

Authors

Alexander Trende¹, Anirudh Unni², Mischa Jablonski², Bianca Biebl³, Andreas Lüdtke¹, Martin Fränzle⁴, Jochem W Rieger²

Affiliations

¹ German Aerospace Center, Institute of Systems Engineering for Future Mobility, Oldenburg, Germany.
² Applied Neurocognitive Psychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.
³ School of Engineering and Design, Technical University of Munich, Garching, Germany.
⁴ Foundations and Applications of Systems of Cyber-Physical Systems, Department of Computing Science, University of Oldenburg, Oldenburg, Germany.

Abstract

Traffic situations like turning at intersections are destined for safety-critical situations and accidents. Human errors are one of the main reasons for accidents in these situations. A model that recognizes the driver's turning intent could help to reduce accidents by warning the driver or stopping the vehicle before a dangerous turning maneuver. Most models that aim at predicting the probability of a driver's turning intent use only contextual information, such as gap size or waiting time. The objective of this study is to investigate whether the combination of context information and brain activation measurements enhances the recognition of turning intent. We conducted a driving simulator study while simultaneously measuring brain activation using high-density fNIRS. A neural network model for turning intent recognition was trained on the fNIRS and contextual data. The input variables were analyzed using SHAP (SHapley Additive exPlanations) feature importance analysis to show the positive effect of the inclusion of brain activation data. Both the model's evaluation and the feature importance analysis suggest that the combination of context information and brain activation leads to an improved turning intent recognition. The fNIRS results showed increased brain activation differences during the "turn" decision-making phase before turning execution in parts of the left motor cortices, such as the primary motor cortex (PMC; putative BA 4), premotor area (PMA; putative BA 6), and supplementary motor area (SMA; putative BA 8). Furthermore, we also observed increased activation differences in the left prefrontal areas, potentially in the left middle frontal gyrus (putative BA 9), which has been associated with the control of executive functions, such as decision-making and action planning. We hypothesize that brain activation measurements could be a more direct indicator with potentially high specificity for the turning behavior and thus help to increase the recognition model's performance.

Keywords: automotive; driving simulator; fNIRS; intention classification; machine learning; neuroergonomics.