Driving maneuvers prediction based on cognition-driven and data-driven method

TL;DR

This paper introduces a novel cognitive and data-driven fusion model, CF-RNN, for predicting driving maneuvers that outperforms previous methods by integrating human cognition with vehicle data.

Contribution

The paper presents the Cognitive Fusion-RNN, a new model that combines inside and outside vehicle data with human reaction time for improved maneuver prediction.

Findings

Outperforms previous methods on Brain4Cars dataset

Achieves state-of-the-art performance in driving maneuver prediction

Effectively fuses cognitive and data-driven information

Abstract

Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.