Sabry, Mohamed (Johannes Kepler University Linz, Austria), Morales-Alvarez, Walter (Johannes Kepler University Linz), Olaverri-Monreal, Cristina (Johannes Kepler University Linz, Austria)

Automated Vehicle Driver Monitoring Dataset from Real-World Scenarios

Scheduled for presentation during the Invited Session "Towards Human-Inspired Interactive Autonomous Driving I" (ThAT2), Thursday, September 26, 2024, 10:30−10:50, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

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. % detection of risky behaviors in order 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.