Faculty Publications
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Item Cyber-Physical Systems: Historical Evolution and Role in Future Autonomous Transportation(Springer Science and Business Media Deutschland GmbH, 2022) Rudra, B.; Thanmayee, S.The revolutionary research and experiments in the field of computing and communicating technologies have resulted in a dramatic impact on the applications with societal and economic benefit. With the evolution of the Internet, it is now possible to connect every object or thing in the physical world. These things can communicate and also perform computations. It is now possible for humans to communicate with the physical things around us. This leads us to explore a whole new technology called Cyber Physical System (CPS). CPS combines computation, communication and control technologies in order to integrate the existing networked systems and embedded systems. It has modules to perform accurate data acquisition. These modules are basically distributed devices. Further the acquired data is sent to a layer of information processing as per the service requirements. There are enormous applications of CPS namely: digital medical devices, autonomous vehicles, robotic systems, intelligent highways, aerospace systems, industry automation, building and environment control and physical process control. Among the listed applications, autonomous transportation is evolving through the ongoing research trends. In autonomous transportation systems, there is a high requirement for reliable communication between the communicating entities, accurate data acquisition and processing and high computing capabilities. Thus CPS is a novel engineering system that can suit the requirements of autonomous transportation. In this chapter we discuss the evolution of CPS and its role in future autonomous transportation. We explore the research challenges in the CPS based on autonomous transportation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Semantic Segmentation for Autonomous Driving(Springer Science and Business Media Deutschland GmbH, 2023) Divakarla, U.; Bhat, R.; Madagaonkar, S.B.; Pranav, D.V.; Shyam, C.; Chandrashekar, K.Recently, autonomous vehicles (namely self-driving cars) are becoming increasingly common in developed urban areas. It is of utmost importance for real-time systems such as robots and automatic vehicles (AVs) to understand visual data, make inferences and predict events in the near future. The ability to perceive RGB values (and other visual data such as thermal, LiDAR), and segment each pixel into objects is called semantic segmentation. It is the first step toward any sort of automated machinery. Some existing models use deep learning methods for 3D object detection in RGB images but are not completely efficient when they are fused with thermal imagery as well. In this paper, we summarize many of these architectures starting from those that are applicable to general segmentation and then those that are specifically designed for autonomous vehicles. We also cover open challenges and questions for further research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Implementing Service-Oriented Game-Theoretic Security Scheme for IoV Networks in Self-driving Cars(Springer Science and Business Media Deutschland GmbH, 2024) Divakarla, U.; Chandrasekaran, K.The security, privacy, and ethical issues surrounding the implementation of connected vehicles (CVs) are numerous. This paper provides a review of recent studies that investigate various IoT-related topics in self-driving cars, such as network architectures, security, and routing. It also suggests the implementation of Service-Oriented Game-Theoretic Security (SOS), which protects against phishing, DoS, and Sybil attacks. Here, we have combined our plan with Named the Networking (NDN), a method that focuses more on the substance and verifies the accuracy of the received data rather than examining the reliability of the sender. In order to accommodate our implementation and simulate the suggested method in a smaller-scale setting, we additionally change the current alert system model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item 2D-VPC: An Efficient Coverage Algorithm for Multiple Autonomous Vehicles(Institute of Control, Robotics and Systems, 2021) Nair, V.G.; Guruprasad, K.R.In this paper, we address a problem of multi-robotic coverage, where an area of interest is covered by multiple sensors, each mounted on an autonomous vehicle such as an aerial or a ground mobile robot. The area of interest is first decomposed into grids of equal size and then partitioned into Voronoi cells. Each robot/sensor is assigned the task of covering the corresponding Voronoi cell. We propose an optimal gridding size and partitioning methodology that eliminate the coverage inefficiencies induced by the partitioning process. We carried out experiments using multiple quadcopters and mobile robots to demonstrate and validate the proposed multi-sensor coverage strategy. © 2021, ICROS, KIEE and Springer.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.