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.

Traffic accidents are a major public safety challenge around the world and are often influenced by the cognitive and physiological states of drivers. Among the multiple in-vehicle factors, listening to music has shown complex effects on driver behavior, particularly in relation to music tempo. This study proposes TransBiNet, a novel deep learning architecture that integrates Transformer-based attention mechanisms with Bidirectional Long Short-Term Memory layers to classify driver states under different auditory conditions using internal biometric signals. Data were collected from 26 participants driving in a simulated environment, where each subject completed scenarios involving fast-tempo music, slow-tempo music, and no music. Physiological signals (heart rate, breathing rate, galvanic skin response, and skin temperature) and head motion data (gyroscope and accelerometer) were gathered via wearable sensors and used as input to the model. The architecture was optimized through Hyperband-based hyperparameter tuning and showed a test accuracy of 97.62% as well as strong precision and recall across all classes. The results showed that internal physiological and motion-based signals are sufficient for robust classification of music-induced driver states, supporting the potential for real-time, sensor-driven driver monitoring systems in intelligent transportation.

Abstract Traffic accidents are a major global health and economic concern. As such, research into understanding driving behaviors becomes essential to minimize the associated risks. Among various factors that can influence driving behaviors, listening to music while driving is a complex task that needs investigation. Music can enhance arousal and manage stress, which could potentially improve one’s driving performance. Listening to music, however, also competes for cognitive resources, increasing one’s mental workload and potentially degrading the driving ability. This study investigates the impact of listening to music with different tempos on drivers and their performance using an Artificial Intelligence (AI) approach. A total of 26 participants are subjected to three driving scenarios in a unique simulated experiment, while utilizing a motion platform. The conditions are driving while listening to slow-tempo music, and fast-tempo music, and with no music. A dataset is created by collecting data through Tobii eye-tracking glasses, Equivital sensor belts, and a software tool. This dataset is preprocessed and used to train a convolutional neural network-long short-term memory (CNN-LSTM) model. This model’s performance is optimized through hyperparameter tuning and Chi-squared feature selection, in order to maximize accuracy and minimize computation time. The model performance is compared with those from a densely layered deep learning model and several classical machine learning models. The devised CNN-LSTM model outperforms other machine learning models, achieving an average accuracy rate of 99.29% with minimal variance across multiple evaluations, demonstrating its effectiveness and consistency in classifying drivers’ behaviors under varying auditory conditions.

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle.

The issue of cognitive distraction during driving has been identified as a major cause of road accidents. Detecting cognitive distraction in real-time can be a valuable strategy for preventing accidents. In this study, a novel approach is presented for the purpose of detecting cognitive distraction in real-time using artificial intelligence while taking into account eye-tracking and head movement data, combined with driving performance measures. This methodology involved collecting data from participants in a driving simulator, on a motion platform, while they performed a cognitive task as well as a control driving scenario. The data collected included eye-tracking data, head movement data, driving performance measures, and subjective ratings of distraction. To develop an accurate model for detecting cognitive distraction, a Deep Feedforward Neural Network (D-FFNN) model was employed while considering binocular gaze direction, pupil diameter, orientation of each eye, head rotational velocities, and head acceleration. The developed model was trained using the collected data and achieved an accuracy of 96.09% in detecting cognitive distraction. The results of our study demonstrate the effectiveness of the proposed method in identifying cognitive distraction in real-time. Also, the accuracy of this model was compared with other AI based classification algorithms. The proposed method has significant implications for preventing vehicle accidents caused by cognitive distraction. The proposed method can be integrated into existing driver-assistance systems to alert drivers and assist them in returning their focus to the road.