Automated Vehicle Driver Monitoring Dataset from Real-World Scenarios

TL;DR

This paper introduces a comprehensive real-world driver activity dataset for autonomous driving scenarios, enabling improved training and benchmarking of deep learning models for driver activity recognition.

Contribution

It provides an openly accessible real-world dataset capturing diverse driver activities under various conditions, addressing limitations of simulation-based datasets.

Findings

Dataset offers diverse real-world scenarios for robust model training

Models trained on this dataset show high accuracy in activity recognition

Benchmark results demonstrate dataset's effectiveness for driver monitoring tasks

Abstract

From SAE Level 3 of automation onwards, drivers are allowed to engage in activities that are not directly related to driving during their travel. However, in level 3, a misunderstanding of the capabilities of the system might lead drivers to engage in secondary tasks, which could impair their ability to react to challenging traffic situations. Anticipating driver activity allows for early detection of risky behaviors, to prevent accidents. To be able to predict the driver activity, a Deep Learning network needs to be trained on a dataset. However, the use of datasets based on simulation for training and the migration to real-world data for prediction has proven to be suboptimal. Hence, this paper presents a real-world driver activity dataset, openly accessible on IEEE Dataport, which encompasses various activities that occur in autonomous driving scenarios under various illumination and weather conditions. Results from the training process showed that the dataset provides an excellent benchmark for implementing models for driver activity recognition.