Faculty Publications
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Item A unified vehicle trajectory prediction model using multi-level context-aware graph attention mechanism(Springer, 2024) Sundari, K.; Senthil Thilak, A.S.Predicting the mobility patterns of vehicles together with their interactions among surrounding traffic objects is a critical task in autonomous driving systems. Existing graph neural network-based trajectory prediction models primarily capture the structural connectivity of network nodes (road objects) and assume equal priority to all neighbors of a node. However, in real-time traffic networks, the behavior of each vehicle is significantly influenced by its neighboring road objects and this influence is not uniform. This necessitates a neighbor interaction-aware trajectory prediction model that assumes non-uniform priority among neighboring nodes. In this article, we have designed a novel unified trajectory prediction model which is suitable for both highway and urban traffic conditions. The proposed approach seamlessly integrates multi-level context modeling using graph attention mechanisms, capturing and leveraging interactions and dependencies among objects at varied levels of proximity within a graph. Additionally, it employs an encoder–decoder long short-term memory architecture for long-term trajectory prediction, ensuring adaptability to different driving scenarios. The advanced graph attention mechanisms play a crucial role in modeling spatial dependencies between vehicles, allowing the proposed model to dynamically adapt to evolving interactions over time. The experimentations done on real-world trajectory datasets, namely, Next Generation Simulation US-101 highway dataset and diverse urban datasets such as ApolloScape and Argoverse demonstrate remarkable performance of MC-GATP in long-term trajectory prediction. The model showcases superior prediction accuracy, scalability, and computational efficiency for both highway and urban environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item A deep learning approach to predicting vehicle trajectories in complex road networks(Springer Science and Business Media Deutschland GmbH, 2025) Sundari, K.; Senthil Thilak, A.S.Accurate prediction of vehicle trajectories is essential for safe and efficient navigation in urban environments, particularly with the increasing prevalence of autonomous vehicles and intelligent transportation systems. This paper introduces a deep learning-based approach for predicting vehicle trajectories on urban roads in real time. The method combines techniques from graph neural networks (GNNs) and long short-term memory (LSTM)-based models to capture intricate spatial and temporal dependencies among vehicles. Vehicles are represented as nodes in the proposed graph model, and graph attention mechanism is used to model the interactions between them. Additionally, LSTM modules encode motion patterns and temporal correlations, facilitating spatial and temporal information fusion to improve prediction accuracy. The effectiveness of the approach is demonstrated through extensive experimentation and evaluation in generating vehicle trajectories, surpassing baseline methods. The proposed method holds promise for real-time vehicle trajectory prediction, with the potential for applications in autonomous driving, traffic management, and intelligent transportation systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.