Browse Topic: Vehicle to infrastructure (V2I)
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
This document describes machine-to-machine (M2M)1 communication to enable cooperation between two or more traffic participants or CDA devices hosted or controlled by said traffic participants. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle equipped with an engaged driving automation system feature and a CDA device. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of conventional vehicles or pedestrians or cyclists carrying compatible personal devices), or compatible road operator devices (e.g., those used by personnel who maintain or operate traffic signals or work zones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the safer and more efficient movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be
In today’s world, Vehicles are no longer mechanically dominated, with increased complexity, features and autonomous driving capabilities, vehicles are getting connected to internal and external environment e.g., V2I(Vehicle-to-Infrastructure), V2V(Vehicle-to-Vehicle), V2C(Vehicle-to-Cloud) and V2X(Vehicle-to-Everything). This has pushed classical automotive system in background and vehicle components are now increasingly dominated by software’s. Now more focus is made on to increase self-decision-making capabilities of automobile and providing more advance, safe and secure solutions e.g., Autonomous driving, E-mobility, and software driven vehicles, due to which vehicle digitization and lots of sensors inside and outside the vehicle are being used, and automobile are becoming intelligent. i.e., intelligent vehicles with advance safe and secure features but all these advancements come with significant threat of cybersecurity risk. Therefore, providing an automobile that is safe and
The integration of Vehicle-to-Everything (V2X) communication technologies holds immense potential to revolutionize the automotive industry by enabling vehicles to communicate with each other (V2V) and with infrastructure (V2I). This paper investigates the feasibility of V2X and V2I communication, exploring available communication methods for vehicles to communicate. Many a times people like to travel together and it involves more than one vehicle travelling together, in such cases they often get lost the information about fellow vehicles due to the traffic condition and different driving behaviors of the individual driver. In such cases they communicate over phones to get to know the location of fellow vehicle or keep sharing their live locations. In such cases they don’t just follow the destination in maps also they should be continuously monitoring their fellow vehicles position. It is important for vehicles travelling in group to have communication and be connected so that they know
This report specifies the minimum requirements for the Road Geometry and Attributes (RGA) data set (DS) to support road geometry related motor vehicle safety applications. Contained in this report are a concept of operations, requirements, and design, developed using a detailed systems engineering process. Utilizing the requirements, the RGA DS is defined, which includes the DS Abstract Syntax Notation One (ASN.1) format, data frames, and data element definitions. The requirements are intended to enable the exchange of the messages and their DS information to provide the desired interoperability and data integrity to support the applications considered within this report, as well as other applications which may be able to utilize the DS information. System requirements beyond this are outside the scope of this report.
The transition to electric vehicles in the transportation sector still faces multiple technological challenges and large investments as regards both vehicle design and vehicle charging infrastructure. Therefore, internal combustion engines still play a role in such a sector, making the engine improvements, in terms of pollutant emissions and efficiency, essential to mitigate the impact of human activities on the environment. One of the possible approaches to improve the efficiency of internal combustion engines is the recovery of the engine exhaust heat, from both the hot exhaust gases and the engine cooling system. In recent years, among the energy recovery strategies, the use of direct injection of H2O under supercritical and superheated thermodynamic states has been explored. Such a technique uses pressurized water recovered from the exhaust gases, heated to high temperature by using the engine exhaust heat and re-injected into the engine combustion chamber. This results in higher
Connected vehicles can provide data from multiple sensors that monitor both the vehicle and the environment through which the vehicle is passing. The data, when shared, can be used to enhance and optimize transportation operations and management—specifically, traffic flow and infrastructure maintenance. This document describes an interface between vehicle and infrastructure for collecting vehicle/probe data. That data may represent a single point in time or may be accumulated over defined periods of time or distance, or may be triggered based on circumstance. The purpose of this document is to define an interoperable means of collecting the vehicle/probe data in support of the use cases defined herein. There are many additional use cases that may be realized based on the interface defined in this document. Note that vehicle diagnostics are not included within the scope of this document, but diagnostics-related features may be added to probe data in a future supplemental document.
This paper explores the efficacy and efficiency of a system for the effective location of electric gridlines during daytime and night-time by the onboard and offboard transceivers of UAV through vehicle to infrastructure communication. The usage of electric gridlines in urban areas helps to extend the range of the UAVs by charging the onboard battery using an extended arm. The same arm can also be used for direct propulsion of the motors onboard UAV, thereby minimizing the reliance on battery. UAVs with advanced Image processing algorithms are utilized in the inspection of the electric grid lines themselves in the Power industry. The camera based algorithms are not effective during night-time when the gridlines are near invisible. This can be mitigated by evaluating light in other spectral ranges, but this would add to the load of the UAV. We propose a system which combines multiple information sources and helps locate the gridlines for range extension, specifically for the delivery of
Vehicle to Everything (V2X) communication has enabled on-board access to information from other vehicles and infrastructure. This information, traditionally used for safety applications, is increasingly being used for improving vehicle fuel economy [1-5]. This work aims to demonstrate energy consumption reductions in heavy/medium duty vehicles using an eco-driving algorithm. The algorithm is enabled by V2X communication and uses data contained in Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) to generate an energy-efficient velocity trajectory for the vehicle to follow. An urban corridor was modeled in a microscopic traffic simulation package and was calibrated to match real-world traffic conditions. A nominal reduction of 7% in energy consumption and 6% in trip time was observed in simulations of eco-driving trucks. Next, track testing of representative velocity profiles was executed based on SAE J1321 recommended practices [6], which showed good agreement with
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