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

TL;DR

This paper introduces a visual AOI-based multimodal trajectory prediction model that improves probabilistic risk assessment at intersections by accurately predicting driving intentions and future trajectories, enabling earlier conflict detection.

Contribution

It presents a novel integration of visual AOI-driven intention prediction with trajectory modeling for enhanced risk assessment in complex traffic scenarios.

Findings

Visual AOI predicts driving intention 0.6-2.1 s ahead.

The intention model predicts steering 0.925 s before action.

The proposed model outperforms existing state-of-the-art methods.

Abstract

Accurate and reliable prediction of driving intentions and future trajectories contributes to cooperation between human drivers and ADAS in complex traffic environments. This paper proposes a visual AOI (Area of Interest) based multimodal trajectory prediction model for probabilistic risk assessment at intersections. In this study, we find that the visual AOI implies the 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 was pre-trained independently to extract the driving intention using features including the visual AOI, historical vehicle states, and environmental context. The intention prediction experiments verify that the visual AOI-based DI model predicts steering intention 0.925 s ahead of the actual steering operation. The trained DI model is then integrated into the trajectory prediction model to filter multimodal trajectories. The trajectory prediction experiments show that the proposed model outperforms the state-of-the-art models. Risk assessment for traffics at intersections verifies that the proposed method achieves high accuracy and a low false alarm rate, and identifies the potential risk about 3 s before a conflict occurs.