Browse Topic: Intelligent transportation systems
Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints. Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and
We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in
The road network is a critical component of modern urban mobility systems, with signalized traffic intersections playing a pivotal role. Traditionally, traffic light phase timings and durations at intersections are designed by transportation engineers using historical traffic data. Some modern intersections employ trigger-based mechanisms to improve traffic flow; however, these systems often lack global awareness of traffic conditions across multiple intersections within a network. With the increasing availability of traffic data and advancements in machine learning, traffic light systems can be enhanced by modeling them as agents operating in an environment. This paper proposes a Reinforcement Learning (RL) based approach for multi-agent traffic light systems within a simulation environment. The simulation is calibrated using real-world traffic data, enabling RL agents to learn effective control strategies based on realistic scenarios. A key advantage of using a calibrated simulation
This article introduces a comprehensive cooperative navigation algorithm to improve vehicular system safety and efficiency. The algorithm employs surrogate optimization to prevent collisions with cooperative cruise control and lane-keeping functionalities. These strategies address real-world traffic challenges. The dynamic model supports precise prediction and optimization within the MPC framework, enabling effective real-time decision-making for collision avoidance. The critical component of the algorithm incorporates multiple parameters such as relative vehicle positions, velocities, and safety margins to ensure optimal and safe navigation. In the cybersecurity evaluation, the four scenarios explore the system’s response to different types of cyberattacks, including data manipulation, signal interference, and spoofing. These scenarios test the algorithm’s ability to detect and mitigate the effects of malicious disruptions. Evaluate how well the system can maintain stability and avoid
The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and
Letter from the Guest Editors
Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The
The escalation of road infrastructure anomalies, such as speed breakers and potholes, presents a formidable challenge to vehicular safety, efficient traffic management, and road maintenance strategies worldwide. In addressing these pervasive issues, this paper proposes an advanced, integrated approach for the detection and classification of speed breakers and potholes. Utilizing a sophisticated blend of deep learning methodologies and enhanced image processing techniques, our solution leverages Object Detection to analyze and interpret real-time visual data captured through advanced vehicle-mounted camera systems. This research meticulously details the comprehensive process involved in the development of this system, including the acquisition and preprocessing of a vast, varied dataset representative of numerous road types, conditions, and environmental factors. Through rigorous training, testing, and validation phases, the model demonstrates remarkable proficiency in recognizing and
The term Software-Defined Vehicle (SDV) describes the vision of software-driven automotive development, where new features, such as improved autonomous driving, are added through software updates. Groups like SOAFEE advocate cloud-native approaches – i.e., service-oriented architectures and distributed workloads – in vehicles. However, monitoring and diagnosing such vehicle architectures remain largely unaddressed. ASAM’s SOVD API (ISO 17978) fills this gap by providing a foundation for diagnosing vehicles with service-oriented architectures and connected vehicles based on high-performance computing units (HPCs). For service-oriented architectures, aspects like the execution environment, service orchestration, functionalities, dependencies, and execution times must be diagnosable. Since SDVs depend on cloud services, diagnostic functionality must extend beyond the vehicle to include the cloud for identifying the root cause of a malfunction. Due to SDVs’ dynamic nature, vehicle systems
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