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Accurate and reliable prediction of driving intentions and futuretrajectories contributes to cooperation between human drivers and ADAS incomplex traffic environments. This paper proposes a visual AOI (Area ofInterest) based multimodal trajectory prediction model for probabilistic riskassessment at intersections. In this study, we find that the visual AOI impliesthe driving intention and is about 0.6-2.1 s ahead of the operation. Therefore,we designed a trajectory prediction model that integrates the driving intention(DI) and the multimodal trajectory (MT) predictions. The DI model waspre-trained independently to extract the driving intention using featuresincluding the visual AOI, historical vehicle states, and environmental context.The intention prediction experiments verify that the visual AOI-based DI modelpredicts steering intention 0.925 s ahead of the actual steering operation. Thetrained DI model is then integrated into the trajectory prediction model tofilter multimodal trajectories. The trajectory prediction experiments show thatthe proposed model outperforms the state-of-the-art models. Risk assessment fortraffics at intersections verifies that the proposed method achieves highaccuracy and a low false alarm rate, and identifies the potential risk about 3s before a conflict occurs.

Visual Area of Interests based Multimodal Trajectory Prediction for Probabilistic Risk Assessment