Alsanwy, Shehab, Qazani, Mohammad Reza Chalak, Shajari, Arian, Nahavandi, Saeid, and Asadi, Houshyar (2025) Enhancing Path Prediction with Eye Movement Data: Deep Learning Applications in Advanced Driver Assistance Systems and Autonomous Vehicles. In: Proceedings of the IEEE International Conference on Systems Man and Cybernetics. pp. 7737-7743. From: SMC 2025: IEEE International Conference on Systems, Man, and Cybernetics, 5-8 October 2025, Vienna, Austria.

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Abstract

Vehicle trajectory prediction plays a pivotal role in enhancing advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), crucial for collision avoidance, path planning, and traffic management. Traditional models often fail to account for variations in driver behaviour, such as eye movement patterns, which can substantially influence trajectory predictions. Our research presents an advanced trajectory prediction model that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks with both eye movement data and traditional vehicle dynamic information. The accuracy of these models was evaluated in a simulated environment designed to mimic real-world driving conditions, capturing extensive data on vehicle dynamics, including position, rotation, acceleration, speed, and eye movement patterns. Data collection was rigorously conducted with 17 drivers, each using a driving simulator that ran the Euro Truck Simulator software. The models were implemented and validated using Python 3.9 and Google Colab, chosen for their effectiveness in handling deep learning tasks. Our findings demonstrate that the inclusion of eye movement data alongside vehicle dynamics enhances the accuracy of trajectory predictions, significantly reducing both Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as well as Mean Absolute Error Percentage (MAPE), compared to models based solely on vehicle dynamics. This improvement not only bolsters the precision of trajectory predictions for ADAS and AV systems but also significantly elevates their safety and operational efficiency.

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