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 collisions under compromised conditions. It also analyzes the impact of varying levels of attack severity on overall system performance. The cooperative navigation framework highlights its potential as a robust solution for secure, efficient, and safe autonomous vehicle operations in increasingly interconnected and potentially hostile environments. Case 1 simulates communication jamming, where all channels except vehicle-to-vehicle communication are compromised. Case 2 extends this to jamming in the smart traffic light system, creating a non-signalized environment. Case 3 represents an ideal scenario with seamless communication. Case 4 explores vulnerability to deliberate interference in actor vehicle velocities, amplifying collision risk. Surrogate optimization with radial functions ensures proactive collision avoidance, while model predictive control with the interior point solver optimizes trajectory planning, promoting collision-free operation, and improving traffic flow. The algorithm’s outputs are seamlessly integrated into the vehicle control system, with the ego vehicle’s dynamics modeled realistically. Through extensive simulations, the algorithm proves effective across diverse scenarios, including communication disruptions and intentional interference. The research contributes to cooperative navigation system advancement, showcasing potential improvements in safety, efficiency, and adaptability in contemporary vehicular environments. The algorithm’s ability to handle various scenarios presents promising prospects for future intelligent transportation systems research.
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 fellow vehicles position and check on where to stop next for food/other purpose. Also immediately let other vehicle know if they are going in a wrong direction. This paper explores solutions that can be applied to the existing 2-wheeler/4-wheeler for simpler ways to stay connected during group travel.
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 secure through cyber-attack is also got equal importance. In this paper, we will discuss some of the challenges and key application of cybersecurity in the automotive sector. We will also discuss some possible approaches to address these challenges and enhance the security and privacy of automotive systems. Certain Automotive cybersecurity applications include Secure ECU communication, Digital signature generation and verification, Secure V2X, In-vehicle infotainment (IVI) security, Secure key management and storage, Secure remote vehicle access and control, and Secure over-the-air (OTA) updates. The main challenges for all these applications are to maintain confidentiality, integrity, and authenticity of the data, which can be maintained using cryptographic algorithms and key management realized in Hardware Security Module (HSM). The HSM is a specialized Hardware component designed and integrated as a part of advanced microcontroller unit (MCU) architecture, dedicated to implement cryptographic security tasks. HSM provide various solutions for secure boot/authenticated boot, secure communication, secure key storage, certificate management, standard encryption / decryption algorithms, which strengthen the mode of algorithm and implements very robust Secured ECU communication.
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 in-chamber pressure, which increases the engine work and efficiency. The injector geometry is a key component of the process, as it determines the structure of the resulting under-expanded jet and the in-chamber flow field, thus affecting the jet interaction with combustion. In this work, three different injector geometries, namely an axial, an open-nozzle and a 4-holes injector, and two injected fluids, i.e. supercritical water and superheated steam, have been considered in order to highlight the advantages and drawbacks of each of them. To this end, a CFD model of a 4-stroke spark ignition internal combustion engine has been used. The results show that the injected supercritical water penetrates faster compared to superheated steam for all three injector geometries. The 4-holes and the open-nozzle injectors present the shortest and the longest penetration time, respectively, with both injected fluids. Besides, the 4-holes injector has given the highest TKE increase, followed by the axial injector and the open nozzle injector. The TKE is, for all cases, three orders of magnitude higher than the case without injection.
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 packages in the Urban Mobility domain. The system utilizes annotated maps for locating any electric grid lines in the vicinity. The finer control needed for placing the extension arm on live electric wire is done using a set of three radio transceivers installed on an electric pole and a double or triple transceiver configuration onboard UAV which locates the live-wire through deductive analysis of sensor data. The trajectory planning subsystem can utilize this information for establishing an efficient route and make multiple deliveries.
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 simulation results. The team also went through an exercise to understand the achievable upper bounds on energy consumption benefits based on a drive cycle synthesized by National Renewable Energy Laboratory (NREL) for Port Drayage application [7]. The velocity trajectory generation using a calibrated traffic simulation, use of offline smoothing routines to understand upper bounds, and track testing based on J1321 procedures contribute towards the novelty of this work.
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