Alsanwy, Shehab, Asadi, Houshyar, Chalak Qazani, Mohama Rreza, Mohamed, Shady, and Nahavandi, Saeid (2023) A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 3097-3103. From: SMC 2023: IEEE International Conference on Systems, Man, and Cybernetics, 1-4 October 2023, Honolulu, HI, USA.

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Abstract

Vehicle trajectory prediction is essential in ensuring the safe and efficient operation of advanced driver assistance systems (ADAS) and autonomous vehicles (AVs), as it enables highly efficient collision avoidance, path planning, and traffic control. However, existing models for vehicle trajectory prediction predominantly focus on limited driving scenarios, resulting in limited applicability. To address this limitation, we present a novel vehicle trajectory prediction approach that employs a Convolutional Long Short-Term Memory (CNN-LSTM) model, incorporating simulated environments and vehicle dynamic time series data, including longitudinal, vertical, and latitudinal position and acceleration. Our approach is distinguished by its ability to handle diverse urban driving scenarios, such as highways, roundabouts, intersections, and turns, which enhances its applicability and generalizability. We experimented and collected vehicle data from 17 drivers using a stationary driving simulator and the Euro Truck Simulator software. For the model implementation and validation, we utilized Python 3.9 and Google Colab, as well as the Scikit-learn library for Deep learning algorithms. The proposed CNN-LSTM model leverages a convolutional layer to learn local patterns and an LSTM layer to capture long-term temporal dependencies, improving performance in predicting vehicle trajectories. The experimental results demonstrate that the CNN-LSTM model provides more accurate predictions for longitudinal and lateral positions compared to traditional vehicle trajectory prediction methods that employ LSTM and Recurrent Neural Network (RNN). This research contributes to developing robust and reliable vehicle trajectory prediction systems vital for ADAS and AVs' safe and efficient operation. The proposed approach broadens the applicability of trajectory prediction models, enabling better-informed decision-making in various driving conditions and ultimately improving road safety and efficiency in the rapidly evolving field of autonomous transportation.

Item ID:	87034
Item Type:	Conference Item (Research - E1)
ISBN:	979-8-3503-3702-0
Copyright Information:	© 2023 IEEE
Funders:	Australian Research Council (ARC)
Projects and Grants:	ARC DE210101623
Date Deposited:	09 Sep 2025 23:11
FoR Codes:	40 ENGINEERING > 4002 Automotive engineering > 400203 Automotive mechatronics and autonomous systems @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 70%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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