Browse Topic: Intelligent transportation systems
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather
The rapid evolution of intelligent transportation systems has made drivers’ attentiveness and adherence to safety protocols more critical than ever. Traditional monitoring solutions often lack the adaptability to detect subtle behavioral changes in real time. This paper presents an advanced AI-powered Driver Monitoring System designed to continuously assess driver behavior, fatigue, distractions, and emotional state across various driving conditions. By providing real-time alerts and insights to vehicle owners, fleet operators, and safety personnel, the system significantly enhances road safety. The system integrates lightweight AI/ML algorithms, image processing techniques, perception models, and rule-based engines to deliver a comprehensive monitoring solution for multiple transportation modes, including automotive, rail, aerospace, and off-highway vehicles. Optimized for edge devices, the models ensure real-time processing with minimal computational overhead. Alerts are communicated
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A smart highway tunnels lighting system based on the technology of cloud platform and Internet of Things(IoTs) has been designed to address the common problems of high energy consumption and low level of intelligence in China's highway tunnel lighting system. The highway tunnel lighting system consists of four layers of architecture: platform management layer, local management layer, middle layer and terminal layer. The system collects real-time brightness, lamp brightness, traffic volume and other data outside the tunnel through various sensors deployed on site, and then uploads the collected data to the main controller through LoRa IoTs. The main controller combines the brightness calculation method of the lighting design rules to control the brightness of the tunnel lighting in real time, achieving real-time adjustment of the brightness of the tunnel LED lights and the brightness outside the tunnel, and realizing a safe and energy-saving lighting effect of "lights on when the car
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With the acceleration of urbanization, freeway traffic congestion is becoming increasingly serious, especially at entrance ramps, where the concentrated inflow of traffic often leads to increased traffic pressure on the mainline, affecting the overall access efficiency. In order to alleviate the ramp congestion problem, this paper proposes a deep reinforcement learning-based intelligent control method for entrance ramps of network-connected vehicles, which adopts Proximal Policy Optimization (PPO) algorithm to optimize the ramp vehicle flow and speed control strategy in real time by constructing a reinforcement learning control framework. In this paper, simulation experiments are conducted in different traffic density scenarios and compared with the traditional reinforcement learning algorithms DQN and A2C. The experimental results show that the PPO algorithm is able to converge quickly in low, medium and high traffic densities, significantly improve the cumulative reward value, and
The traditional hydraulic braking system with vacuum booster technology is very mature, but it is not suitable for use in electric vehicles due to the lack of a vacuum source. The brake system by wire is an innovative electronic controlled braking technology, and the Electro-Hydraulic Brake is currently the most widely used brake system by wire in electric vehicles. The classification, structure, working principle, and advantages of Electro-Hydraulic Brake as a braking system for electric automobiles and intelligent connected vehicles are studied. The structure, working principle, advantages and disadvantages of Pump-Electro - Hydraulic Brake and Integrated Electro-Hydraulic Brake are compared and analyzed.
When identifying the content of this report, one of the goals was that it supports a nationally interoperable method for connected vehicles (CVs) to make traffic signal priority and/or preemption (TSPP) requests of connected intersections (CIs) that support priority and/or preemption services. Given that, this report specifies the over-the-air (OTA) interface between CVs and CIs to support TSPP applications using updated revisions of the SAE J2735 Signal Request Message (SRM) and Signal Status Message (SSM) and the use of a Wireless Access in Vehicular Environments (WAVE) Service Advertisement (WSA) to advertise support for TSPP at a CI. Included are a concept of operations, requirements, design, and message structure definitions developed using a detailed systems engineering process.
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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
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