Browse Topic: Vehicle to vehicle (V2V)

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Cooperative Edge Extension for Automated Driving Level 4 and Beyond2026-01-07767/1/2026
Electrical/Electronic Architectures (EEAs) are continuously evolving to meet newly emerging demands. In recent years, major drivers of this evolution have been the increasing software-defined nature of vehicles and the push toward automated driving. Key technologies such as edge-enhanced functions, vehicle-to-vehicle communication, and service-oriented architectures are therefore the focus of current research efforts. This paper presents a vision of how these technologies can be used to enable cooperation between vehicles, illustrated by using parked vehicles as edge nodes. These are typically seen as obstructions, as they significantly increase the risk of missing or misinterpreting vulnerable road users such as pedestrians or cyclists. Our proposed approach to counteract this problem is the use of the parked vehicles themselves as edge nodes that support object detection or even trajectory planning. Current research primarily considers smart traffic infrastructure, roadside units, and other vehicles as potential edge nodes. Including parked vehicles as edge nodes means that, instead of acting solely as obstacles, we leverage their built-in sensors to contribute to cooperative awareness. While such cooperation will enhance the safety of automated vehicles in urban areas, several challenges arise. In this paper, we discuss how data traceability, decision-making in the presence of conflicting information, and incentive mechanisms for owners of parked vehicles can be addressed. Based on these challenges, the paper outlines requirements for future cooperative architecture and highlights the role of edge-enhanced functions, Vehicle-to-Vehicle (V2V) communication, and service-oriented architectures in enabling fully automated driving.
Lüntzel, Vitus, Lukezic, Nikola, Kraus, David, Seidel, Luca, Beck, Maximilian, Schindewolf, Marc, Sax, Eric
Impact of Network-Induced Delays on Autonomous Vehicle Steering Stability10-10-03-00246/25/2026
This article presents a cross-layer framework that integrates realistic vehicle-to-network-to-vehicle (V2N2V) delay characterization with a rigorous stability analysis of automated vehicle steering control. Both constant and network-induced time-varying delays modeled via deterministic bounds are addressed. For constant delays, delay-independent stability regions within the controller gain space are analytically derived. For time-varying delays with stochastic network origins, modeled using deterministic bounds, a refined Lyapunov–Krasovskii functional (LKF) incorporating augmented single- and double-integral terms is constructed. To establish delay-dependent linear matrix inequality (LMI) conditions, a reciprocally convex combination approach is employed to handle the delay interval partitioning, and the second-order Bessel–Legendre inequality is applied to tighten the integral quadratic bounds. The resulting LMI conditions explicitly capture the coupled effects of delay magnitude, delay variation rate, and control gains on closed-loop stability. Simulations of a lane-keeping scenario confirm that the predicted stability boundaries accurately match the closed-loop system behavior. Notably, incorporating a realistic time-varying V2N2V delay profile into the controller design reduces the lateral-state root-mean-square error (RMSE) by over 54% and decreases the settling time by a factor of 10 compared to designs relying on an average-delay assumption. However, high packet loss rates are shown to still induce residual oscillations due to information scarcity. Ultimately, these results elucidate delay-induced instability mechanisms and provide practical guidelines for designing delay-robust steering controllers for connected and automated vehicles.
Li, Jialin, Lu, Jianwei, Wei, Heng, Ao, Di
An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging2026-99-07305/15/2026
As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.
Liao, Peng, Guo, Jiahe, Ning, Donghong, Li, Sijia, Wang, Tao
A Simple Analytical Model for Predicting Cooperative Ratio in Agreement-Seeking Cooperative Driving Automation with C-V2X Communication2026-01-00784/7/2026
Cooperative Driving Automation (CDA) has emerged as an active research area in recent years, categorized into four classes of operations with varying levels of cooperation as defined in the SAE J3216 standard. Among these, Class C CDA, referred to as Agreement-Seeking Cooperation (ASC), has received limited attention in literature. Unlike Cooperative Adaptive Cruise Control (CACC), which typically engages when lead vehicles are identified as cooperative and disagree under manual override or safety-critical conditions, ASC requires agents to exchange messages interactively to reach consensus on a proposed plan and its implementation. This necessitates more sophisticated communication and control designs, which in turn influences customized ASC efficiency. Previous work has examined, through simulation, the impact of three key parameters on ASC system performance: CDA message transmission frequency, Packet Drop Ratio (PDR), and Cooperation Duration Length (CDL). In this paper, we extend that investigation by conducting Hardware-in-the-Loop (HIL) experiments in a scenario-based simulation environment, integrating the ASC controller with vehicle-to-vehicle (V2V) communication enabled by PC5-based Cellular Vehicle-to-Everything (C-V2X) radios. Using HIL test data, we derived a simple analytical model based on Pascal Distribution to predict the Cooperative Ratio (CR), which is a key index defined by cooperative time over the total scenario time. The model explores the fundamental mechanism of how transmission frequency, total trip time, instance CDA engagement probability and CDL collaboratively impact Cooperative Ratio. The validation of the model with experimental data reports the relative error is less than 5% for scenarios with CDA message transmission frequency higher than 5 HZ. Furthermore, because the model is independent of specific control logic assumptions, it provides a practical tool for guiding the design of ASC communication protocols and control strategies.
Zhan, Lu, Di Russo, Miriam, Das, Debashis, Stutenberg, Kevin, Misra, Priyash, Jeong, Jongryeol, Hyeon, Eunjeong
Characterizing the Impact of C-V2X PC5 Radio on Cooperative Driving Automation in Hardware-in-the-Loop Experiments2026-01-00774/7/2026
The SAE J3216 standard defines Cooperative Driving Automation (CDA), which has received increasing attention in recent years as an umbrella framework encompassing a wide range of automated vehicle applications enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) technologies. Despite this growing interest, limited research has investigated the impact of Cellular Vehicle-to-Everything (C-V2X) on CDA applications, particularly with respect to agreement-seeking operations. This work presents a hardware-in-the-loop (HIL) experimental study designed to evaluate an Argonne National Laboratory designed CDA controller under different message configurations and varying C-V2X PC5 radio transmission frequencies. A three-vehicle car-following scenario was implemented in the Argonne-developed Roadrunner simulator, incorporating CDA agreement-seeking logic, vehicle powertrain models, and V2V communication modules. CDA messages were exchanged through two physical C-V2X PC5 radios, capturing realistic communication impairment caused by the hardware characteristics. Packet loss and cooperation ratio were evaluated as functions of transmission frequency and message scheduling strategy. To further investigate the role of packet loss in the agreement-seeking process, a four-state Markov chain model was applied to characterize mechanisms that reduce cooperation ratio. The results indicate that synchronous transmission of CDA messages introduces half-duplex constraints, leading to increased packet loss and reduced cooperative driving duration. Increasing the message transmission frequency improves overall cooperation time, although it results in higher packet loss rates. These findings provide insight into the behavior of C-V2X radios and their impact on CDA applications, with a particular emphasis on cooperation duration, while remaining agnostic to specific controller performance characteristics.
Zhan, Lu, Di Russo, Miriam, Das, Debashis, Stutenberg, Kevin, Misra, Priyash, Jeong, Jongryeol, Hyeon, Eunjeong
Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach2026-01-04634/7/2026
The advancement of Cooperative Adaptive Cruise Control (CACC) technology enables vehicle platooning on public roads, offering significant potential to enhance urban mobility, driving safety, and energy efficiency. Among various applications, truck platooning has become a promising strategy to increase highway flow rates by reducing vehicle headways, improving coordination, and optimizing space utilization. This paper presents a quantitative assessment of a CACC-based truck platooning system, focusing on its effectiveness in enhancing highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulations of real-world highway networks to identify key influencing factors and evaluate the resulting improvements in traffic flow. The analysis considers five primary variables: desired platoon speed, platoon size, space headway, percentage of platooning trucks, and non-platoon traffic flow. The study systematically examines the impact of each parameter on overall traffic throughput. Results indicate that truck platooning can increase highway flow rates by up to 200%, particularly under conditions of high truck volumes and larger platoon sizes. Both platoon size and the percentage of platooning trucks show a positive correlation with flow rates, suggesting that greater coordination among vehicles enhances overall mobility. Conversely, higher desired speeds and larger space headways tend to diminish the benefits of platooning by reducing traffic density. Overall, this paper provides a comprehensive quantitative evaluation of the mobility benefits of truck platooning and highlights its potential to significantly improve highway operations. Future work will extend these findings to assess the energy and emission benefits of platooning and to evaluate the performance of large-scale platooning deployment strategies.
Karbasi, Amir Hossein, Wang, Jinghui, Yang, Hao
Vehicle-Level Validation Test Procedures for V2V Safety CommunicationsJ3161/1A_202602 (Current)2/11/2026
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in SAE J3161/1. For the purposes of this report, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured LTE-V2X vehicle-to-vehicle (V2V) device as defined in SAE J3161/1 and is installed on a vehicle of class 2, 3, 4, or 5. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle level, so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J3161/1 is satisfied. This document also includes a Traceability Matrix to provide traceability between SAE J3161/1 sections and the test procedures. This can be used to ensure thoroughness of testing coverage. The traceability matrix in Section 7 correlates test procedures from this document with the sections in SAE J3161/1 being tested. The SAE J3161/1 sections that are addressed in this document are indicated in Table 1. SAE J3161/1 major section numbers that are indicated as N/A do not contain any requirements (subsections may include requirements). Sections that are not in scope, such as standards profiles, are expected to be tested and verified as part of device-level certification, done prior to the vehicle-level testing described in this document.
C-V2X Technical Committee
Aircraft Lightning ZoneARP5414B (Current)2/6/2026
This SAE Aerospace Recommended Practice (ARP) defines lightning strike zones and provides guidelines for locating them on particular aircraft, together with examples. The zone definitions and location guidelines described herein are applicable to Parts 23, 25, 27, and 29 aircraft. The zone location guidelines and examples are representative of in-flight lightning exposures.
AE-2 Lightning Committee
Vehicle-to-Vehicle Negotiation Via Manoeuvre Coordination Messages for Cooperative Cut-in Scenarios2026-26-02741/16/2026
The automotive industry is rapidly extending the capabilities of automated systems by incorporating connectivity and cooperation features that enable real-time information exchange between vehicles and road infrastructure. Within the Connected, Cooperative, and Automated Mobility (CCAM) framework, Vehicle-to-Vehicle (V2V) communication is expected to play a key role in improving road safety, traffic efficiency, and driving comfort. This work addresses a practical implementation of the standardized Manoeuvre Coordination Messages (MCMs), as defined in the ongoing ETSI standard (ETSI TS 103 561). The proposed approach is demonstrated through a cooperative cut-in use case in which two vehicles negotiate a lane change manoeuvre. In the considered scenario, the ego vehicle, driven by a Highway Pilot (HWP) system, receives the intention to cut-in from a neighbouring cooperative vehicle through an MCM. In response, the ego vehicle adapts its behaviour by decelerating to generate a safe longitudinal gap, which allows the cooperative vehicle to merge the ego’s lane. The negotiation process relies on the bidirectional exchange of MCMs to coordinate the timing and trajectories, ensuring both vehicles complete the manoeuvre safely. Additionally, the Cooperative Awareness Messages (CAMs) allow the vehicles to share real-time information such as position, speed and heading. This connected-enhanced approach extends the capabilities of local perception systems, enabling an improved performance and reaction time to surround traffic participants. The described use case is implemented and validated in a prototype vehicle equipped with V2V communication capabilities and a Highway Pilot (HWP) SAE level 3 driving automation system. Proving ground tests demonstrate that the system can successfully negotiate cut-in manoeuvres in real time, enhancing both safety and traffic flow. The results confirm the feasibility of deploying standardized V2V coordination mechanisms within operational automated driving functions and lay the groundwork for broader integration into future CCAM applications.
Leiva Ricart, Gisela, Domingo Mateu, Bernat
Testing Framework and Safety Evaluation for Vehicle-to-Vehicle (V2V) Charging Systems in Electric Mobility2026-26-01761/16/2026
This paper presents a comprehensive testing framework and safety evaluation for Vehicle-to-Vehicle (V2V) charging systems, incorporating advanced theoretical modeling and experimental validation of a modern, integrated 3-in-1 combo unit (PDU, DCDC, OBC). The proliferation of electric vehicles has necessitated the development of resilient and flexible charging solutions, with V2V technology emerging as a critical decentralized infrastructure component. This study establishes a rigorous mathematical framework for power flow analysis, develops novel safety protocols based on IEC 61508 and ISO 26262 functional safety standards, and presents comprehensive experimental validation across 47 test scenarios. The framework encompasses five primary test categories: functional performance validation, power conversion efficiency optimization, electromagnetic compatibility (EMC) assessment, thermal management evaluation, and comprehensive fault-injection testing including Byzantine fault scenarios. Through systematic experimental validation using advanced power electronics simulation and hardware-in-the-loop (HIL) testing, we demonstrate 98.2% power conversion efficiency, sub-50ms fault detection response times, and compliance with automotive safety integrity level ASIL-D requirements. Our results establish the theoretical foundations and practical validation methodologies essential for next-generation V2V charging infrastructure deployment.
Uthaman, Sreekumar, Mulay, Abhijit B, Nikam, Sandip B.
Integrating V2V and V2I Communication for Enhanced Traffic Safety and Efficiency in Connected Vehicular Networks2026-26-02711/16/2026
This paper is a new approach to improve road safety and traffic flow by combining vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The Study is focused on a system that connects vehicles with each other and with traffic light to share real-time data about speed and position. This work is aimed to discuss the methodology adopted for developing a system which predicts and advises the optimal speed for vehicles approaching an intersection. Inspired by the Green Light Optimized Speed Advisory (GLOSA) , the proposed system is designed to help drivers approach traffic signals at speeds that minimize unnecessary stops, reduce delays, and improve traffic efficiency. This paper contains the approach taken, the decision-making algorithm, and the simulation framework built in MATLAB/Simulink to validate the concept under real traffic conditions. Simulation results are presented to demonstrate how the system generates speed recommendations based on vehicle parameters and traffic light states. We have worked on the integration of both V2V and V2I communications, combined with a speed advisory algorithm. This work paves the way for smarter, more responsive traffic management systems and supports the future deployment of connected and autonomous vehicles.
Pinto, Colin Aubrey, Shah, Ravindra, Karle, Ujjwala
Integration and Proving Ground Evaluation of a Connected Highway Pilot System2026-26-01361/16/2026
Highway Pilot (HWP) systems, classified as SAE Level 3 Automated Driving Systems (ADS), represent a potential advancement for safer and more efficient highway drives. In this work, the development of a connected HWP prototype is presented. The HWP system is deployed in a real test vehicle and designed to operate autonomously in highway environments. The implementation presented in this paper covers the complete setup of the vehicle platform, including sensor selection and placement, hardware integration and communication interfaces for both autonomous functionality and Vehicle-to-Everything (V2X) connectivity. The software architecture follows a modular design, composed of modules for perception, decision-making and motion control to operate in real-time. The prototype integrates Vehicle-to-Vehicle (V2V) communication, such as Cooperative Awareness Messages (CAM), to enhance situational awareness and improve the overall system behaviour. The modular structure allows new functionalities to be developed and integrated into the same platform, so it becomes a beta testing platform for the early-stage experimentation of innovative features. To validate the prototype, execution of scenario-based testing on a proving ground is used, which is part of the Safety Assurance Framework (SAF), which provides structured methods to help define test scenarios to cover the system’s Operational Design Domain (ODD). Experimental results demonstrate the prototype’s ability to operate in varied highway scenarios.
Domingo Mateu, Bernat, Leiva Ricart, Gisela, Facerias Pelegri, Marc, Perez, Marc
Dynamic Switching Control of Vehicle Platoon with Kalman Filter Compensator Considering Communication Delays and Packet Dropouts2025-01-734112/31/2025
This paper presents a dynamic switching control strategy for vehicle platoons to address communication delays and packet dropouts in connected and autonomous vehicle systems. The proposed strategy combines adaptive cruise control (ACC), cooperative adaptive cruise control (CACC), and a Kalman filter to compensate for time-varying delays, while employing an equidistant spacing policy to support reliable information flow within the platoon. A switching mechanism based on an acceleration threshold enables seamless transition between ACC, which depends on onboard sensor data, and CACC, which relies on vehicle-to-vehicle (V2V) communication. This design reduces dependence on V2V communication, thereby lowering the risk of packet dropouts and improving platoon stability. The control architecture adopts a hierarchical structure: an upper-level sliding mode controller generates desired acceleration commands, and a lower-level PID controller converts them into throttle and brake actions. A Kalman filter is further applied to predict and compensate for sensor and communication delays, where time-varying delays are modeled using a Bernoulli random process. Simulation studies on a five-vehicle platoon show that the proposed strategy effectively suppresses speed fluctuations and maintains safe inter-vehicle distances. Compared with cases without delay compensation, spacing deviations are reduced by 72.4% and velocity deviations by 59.6%. These results demonstrate the potential of the proposed method to improve platoon control under non-ideal communication conditions.
Pan, Deng, Yao, Zhiyong, Wang, Chang, Ji, Jie, Zhang, Bohan
Toward Enhanced Goods Tracking in Commercial Vehicle Interiors through AI and Sensor Integrations12-09-02-001612/23/2025
The efficient tracking and management of goods within light commercial vehicles (LCVs) is crucial for various industries, particularly craftsmen and parcel delivery services. This article explores the integration of artificial intelligence (AI) and sensor technologies to enhance item tracking and optimize logistical operations in LCVs. Two technological approaches are examined: a Bluetooth-based tracking system and a camera-based parcel identification framework. The Bluetooth-based solution is designed primarily for craftsmen. It employs Bluetooth tags, vehicle connectivity gateways (VCGs), and a centralized server to provide real-time inventory monitoring and prevent tool misplacement. The camera-based system is aimed at parcel carriers. It utilizes AI-driven object detection and pose estimation to localize and identify parcels within the vehicle. Experimental evaluations show that Bluetooth tracking ensures reliability in tool management and the AI-based vision system holds promise for future scalability in parcel logistics. The findings underscore the need for adaptive tracking methodologies to improve efficiency, reduce operational costs, and support the digital transformation of commercial vehicle ecosystems.
Aslandere, Turgay, Lens, Mathijs, Kirchhof, Jörg Christian, Robberechts, Pieter, Grein, Marcel, Meert, Wannes, Vandewalle, Patrick, Davis, Jesse, Rumpe, Bernhard, Goedemé, Toon
Vehicle to Vehicle Recharge and Power Sharing for Modular Public Transportation Systems: Design and Testing of the UNIFI fleet at ENEA CASACCIA Research Center2025-32-009511/3/2025
The synergistic adoption of automated driving technologies and the electrification of the vehicle power train offers the possibility of proposing new and innovative solutions for public transportation systems. In particular, an interesting solution is represented by modular systems in which multiple autonomous vehicles/transportation modules can be aggregated to form reconfigurable compositions according to desired transportation demand. In this work, a configurable connection between vehicles is adopted, as convoying ensures the possibility of power sharing between vehicles, allowing coordinated power management throughout the composition. Connected vehicles can also share power between batteries for battery recharge that is performed using a custom solution from a tram-like catenary. In this work, the authors design a demonstrator to investigate the feasibility of the proposed solution. Once designed, the proposed system has been assembled and tested at the ENEA Casaccia Research Center, preliminarily validating the proposed solution.
Alessandrini, Adriano, Berzi, Lorenzo, Fabbri, Marco, Franci, Michael, Gulino, Michelangelo Santo, Pugi, Luca, Ortenzi, Fernando, Vitiello, Francesco
A Hybrid Framework for Kinematic Vehicle Prediction: You Only Look Once version 11 with Unscented Kalman Filter12-09-02-001310/22/2025
In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction using short-horizon kinematic cues. The publicly employable Lyft Level 5 dataset has been used to validate the proposed method, indicating its efficacy in attaining high prediction accuracy with low latency. The experimental results illustrate that the accuracy, precision, root mean square error (RMSE), and mean absolute error (MAE) are improved by 4.1%, 2.66%, 11.9%, and 13.3%, respectively, when the performance of the enhanced algorithm is compared to that of the YOLOv11 combined with extended Kalman filter (EKF) algorithm. Integrating YOLOv11 with the UKF leads to enhanced responsiveness and reliability of vehicle trajectory predictions, which is profitable for autonomous vehicles and advanced driver-assistance systems.
Pahal, Sudesh, Nandal, Priyanka
Improving Speed Trajectory Optimization for Electric Vehicles with Pre-Calculated Energy Maps and Dynamic Profile Discretization2025-01-03187/2/2025
Ongoing research and development in the field of electric vehicles (EVs) have resulted in a continuous expansion of their range. Additionally, advancements in vehicle connectivity have created new opportunities for intelligent driving assistance and energy optimization, particularly through the use of cloud data. However, the integration of eco-driving assistance with numerical optimization of speed trajectories remains challenging due to the high computational demands of these methods. To address this challenge and make such a system feasible for integration into vehicle systems, the computational effort required for an optimized driving trajectory must be minimized. This paper presents a method to accelerate speed trajectory optimization using pre-calculated energy and time consumption maps. For this purpose, a dynamic discretization of the anticipated driving profile is applied. Initial results show a substantial reduction in computation time, varying with different scenarios. Furthermore, trajectory optimizations have led to energy consumption reductions between 5% and 30% in simulation trials. The structure of this paper is as follows: First, the vehicle model, implemented in MATLAB/Simulink, and the longitudinal dynamics, with a focus on key components, are explained. Next, the numerical optimization implementation using Dynamic Programming, the Ford-Bellman Algorithm, and the necessary discretization of the driving profile to be optimized are described. This is followed by an explanation of the hybrid energy consumption calculation. Then, the functional implementation of the algorithm in MATLAB pseudocode is presented. Various optimized driving trajectories for a predefined route are then generated with the optimizer, and the simulation results are analyzed, including the computational effort required for optimization. Finally, the results are summarized, and an outlook on the optimizer’s potential future applications is provided.
Schilling Johnson, Rene, Henke, Markus
Applying Real World Traffic Data on Machine Learning Methods for Graph Based Cooperation Strategies of Automated Vehicles2025-01-02867/2/2025
With the increasing distribution of smart mobility systems, automated & connected vehicles are more and more interacting with each other and with smart infrastructure using V2X-communication. Hereby, the vehicles’ position, driving dynamics data, or driving intention are exchanged. Previous research has explored graph-based cooperation strategies for automated vehicles in mixed traffic environments based on current V2X-communication standards. Thereby, the focus is set on cooperation optimization and maneuver negotiation. These strategies can be implemented through both centralized and decentralized computational approaches and are conflict-free by design. To enhance these previously established cooperation models, real-world traffic data is used to derive vehicle trajectories, providing a more accurate representation of actual traffic scenarios in order to enhance the practical application of the described methodology. Additionally, machine learning algorithms are employed to train the cooperation algorithms with the derived trajectories, enabling further optimization of traffic flow in direct vehicle to vehicle interaction. The application of different machine learning techniques allows for a comparison of their performance in optimizing traffic scenarios with regard to each other and conventional optimization approaches. This study builds on prior work [1], integrating real-world data and advanced algorithms to refine cooperative strategies for automated vehicles, ultimately improving the efficiency of smart mobility systems as shown by a multi-vehicle simulation. This approach shows the potential for enhanced cooperative distributed vehicle control and thereby increases safety and efficiency in such transportation networks significantly. Hereby, the overall cooperation scenario is optimized with regard to the cumulated maneuver execution time and energy consumption of the involved automated and connected vehicles.
Flormann, Maximilian, Meyer, Felix, Henze, Roman
Assessment of Cooperative Navigation Algorithms for CAVs in Threat Scenarios12-09-01-00076/14/2025
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.
Khan, Rahan Rasheed, Hanif, Athar, Ahmed, Qadeer
Letter from the Guest Editors12-08-02-00115/6/2025
Letter from the Guest Editors
Liang, Ci, Törngren, Martin
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202504 (Current)4/14/2025
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 used to influence (directly or indirectly) DDT performance by one or more nearby road users. Vehicles and infrastructure elements engaged in cooperative automation may share information, such as state (e.g., vehicle position, signal phase), intent (e.g., planned vehicle trajectory, signal timing), or seek agreement on a plan (e.g., coordinated merge). Cooperation among multiple participants and perspectives in traffic can improve safety, mobility, situational awareness, and operations. However, nothing in this document is intended to suggest that driving automation requires such cooperation in order to be performed safely. Cooperative strategies may be enabled by the sharing of information in a way that meets the needs of a given CDA Feature. The needs may be expressed in terms of performance characteristics, such as latency, information flow, range, privacy and security, and information content and quality. There are several potential technologies for communicating information between a subject vehicle(s) and other traffic participants. When referring to a CDA Feature, the word “feature” here varies from its common English definition. To emphasize this variance and to avoid confusion, “Feature” is capitalized when modified by the adjective “CDA” or “C-ADS.” This document focuses on CDA Feature-oriented functionality and does not imply use of any specific communications protocols. This document addresses the performance of functions and actions in conducting the DDT (refer to the current version of ISO/SAE PAS 22736/SAE J3016) by the ADS. This information report is intended to facilitate communication and awareness for the design and anticipated development and validation of CDA Features.
Cooperative Driving Automation(CDA) Committee
Predicting Real-time Crash Risks during Hurricane Evacuation Using Connected Vehicle Data09-13-01-00064/12/2025
Hurricane evacuations generate high traffic demand with increased crash risk. To mitigate such risk, transportation agencies can adopt high-resolution vehicle data to predict real-time crash risks. Previous crash risk prediction models mainly used limited infrastructure sensor data without covering many road segments. In this article, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data that contain vehicle speed and acceleration information collected at a high frequency (mean = 14.32, standard deviation = 6.82 s). The dataset was extracted from a database of connected vehicle data for the evacuation period of Hurricane Ida on Interstate-10 in Louisiana. Five machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data. The results indicate that the Gaussian process boosting and extreme gradient boosting models outperform (recall = 0.91) other models. Such real-time crash prediction models, leveraging connected vehicle data, could enable transportation agencies to implement proactive countermeasures, such as dynamic speed limit or lane management, during emergency evacuations, thereby enhancing road safety.
Syed, Zaheen E Muktadi, Hasan, Samiul
Barrier Weight Prediction for Vehicle Compatibility in North America Market Using Finite Element Analysis2025-01-86214/1/2025
The proliferation of the electric vehicle (EVs) in the US market led to an increase in the average vehicle weight due to the assembly of the larger high-voltage (HV) batteries. To comply with this weight increase and to meet stringent US regulations and Consumer Ratings requirements, Vehicle front-end rigidity (stiffness) has increased substantially. This increased stiffness in the larger vehicles (Large EV pickups/SUVs) may have a significant impact during collision with smaller vehicles. To address this issue, it is necessary to consider adopting a vehicle compatibility test like Euro NCAP MPDB (European New Car Assessment Program Moving Progressive Deformable Barrier) for the North American market as well. This study examines the influence of mass across vehicle classes and compares the structural variations for each impact class. The Euro NCAP MPDB (European New Car Assessment Program Moving Progressive Deformable Barrier) protocol referenced for this analysis. Our evaluation approach comprises of two sections: (i) The impact of the barrier mass on to the vehicle structure (V2B) and (ii) vehicle-to-vehicle impact (V2V) analysis. To predict the correct segment weight representing the barrier weight for the North American market, we analyzed the 2022 year to sales data for North American market to assess the average vehicle weight. Data was then compared with CAE predictions barrier mass (~1500kg- 1600kg). Based on the sales data, the average vehicle mass found to be ~1500 kg, aligning with the CAE simulations predictions. This study aims to determine the US sales volume and conduct CAE simulations to predict the precise weight of the barrier that replicates the lightest vehicle currently available in the North American market, as per this study, a barrier weight of (~1500 -1600) kg can be considered for the future vehicle compatibility for NA market. This predicted vehicle weight aligns with the typical vehicle sale data for North American market. Background In 2022, Electric Vehicles (EVs) constituted 14% of all car sales globally, with every major market experiencing year-over-year sales increase. In United States, this meant that over one in five cars sold were electric. To improve the range of EVs, Original Equipment Manufacturers (OEMs) are significantly increasing the weight of high-voltage battery assemblies, leading to an overall increase in the vehicle weight, this in turn, results in substantial increase in front-end-stiffness of the vehicle to meet other US vehicle regulations. The heightened front-end-stiffness is expected to significantly impact vehicle compatibility. To understand various parameters such as Occupant Loading Criterion (OLC) and Standard Deviation (deformation of barrier profile), we referenced Euro NCAP MPDB (European New Car Assessment Program Moving Progressive Deformable Barrier) test protocol. This helped determine the weight of the barrier that represents the small vehicle segment in the North American market. Vehicle-to-Vehicle (V2V) impact CAE simulations were also conducted, selecting smaller vehicle as target vehicles (to represent the barrier mass), and impacting them with different vehicle classes such as Pick-up trucks, Large SUVs, and Compact SUVs. The impact vehicle mass varied from 1800kg to 3200kg.The primary aim of this study is to estimate the barrier weight that replicates the lightest vehicle currently available in the North American market.
Kusnoorkar, Harsha, Koraddi, Basavaraj, Guerrero, Michael, Sripada, Venu Vinod, Tangirala, Ravi
Reducing Power Consumption through Effective Data Sharing in Platooning Scenarios2025-01-80514/1/2025
Platooning occurs when vehicles travel closely together to benefit from multi-vehicle movement, increased road capacity, and reduced fuel consumption. This study focused on reducing energy consumption under different driving scenarios and road conditions. To quantify the energy consumption, we first consider dynamic events that can affect driving, such as braking and sudden acceleration. In our experiments, we focused on modeling and analyzing the power consumption of autonomous platoons in a simulated environment, the main goal of which was to develop a clear understanding of the different driving and road factors influencing power consumption and to highlight key parameters. The key elements that influence the energy consumption can be identified by simulating multiple driving scenarios under different road conditions. The initial findings from the simulations suggest that by efficiently utilizing the inter-vehicle distances and keeping the vehicle movements concurrent, the power consumption of each vehicle within the platoon can be reduced. Moreover, we explored the influence and impact of vehicle-to-vehicle coordination and communication delays on the power consumption. Additionally, controlling speed changes and reducing unnecessary braking can enhance energy efficiency within the platoon, resulting in significant cost savings over time. This study focused on understanding the importance of including detailed power consumption models in the design of a collaborative vehicular environment via information exchange. Incorporating these models can allow platoons to reduce overall power consumption through efficiently designed data sharing and control strategies. The results of this study can help advance automotive technologies in terms of vehicle data exchange and communication, thereby providing a strong foundation for future transportation-related research.
Khalid, Muhammad Zaeem, Azim, Akramul, Rahman, Taufiq
Collision Avoidance System at Urban Intersections Using V2X Communication2025-01-80304/1/2025
Driving safely through urban intersections is quite challenging for self-driving vehicles due to complicated road geometries and the highly dynamic maneuvers of oncoming traffic, which can cause a high risk of collisions. Traditional onboard sensors like cameras, radar, and lidar give limited visibility in this environment. To overcome these limitations, this paper explores implementing a collision avoidance system at urban intersections utilizing vehicle-to-everything (V2X) communication. The system leverages V2X map data to identify warning zones and uses vehicle-to-vehicle (V2V) communication to estimate the maneuver types and paths of approaching vehicles. The system assesses collision risk by calculating the intersection points of predicted paths for the ego vehicle and oncoming traffic. Depending on the level of collision risk, the system generates a collision warning signal to the driver or activates emergency braking when necessary to prevent accidents. We implement the system using Simulink and the RoadRunner scenario simulation tool. To evaluate system performance in a virtual simulation environment, we create a complex urban scene with intersections and traffic signals using RoadRunner. The high-definition (HD) map from RoadRunner is converted into V2X map messages compliant with the T/CSAE 53-2020 standard. RoadRunner’s Signal Tool configures junction signalization and traffic phases. RoadRunner Scenario uses this traffic signal phase information to simulate traffic signals, which are then converted into V2X SPaT (Signal Phase and Timing) messages.
Park, Seo-Wook, Suresh, Raynier, Ailuri, Anusha
Vehicle–Vehicle Collaborative Control Strategy for Dedicated Lanes of Connected Vehicles12-08-02-00123/28/2025
To alleviate the problem of reduced traffic efficiency caused by the mixed flow of heterogeneous vehicles, including autonomous and human-driven vehicles, this article proposes a vehicle-to-vehicle collaborative control strategy for a dedicated lane in a connected and automated vehicle system. First, the dedicated lane’s operating efficiency and formation performance are described. Then, the characteristics of connected vehicle formations are determined, and a control strategy for heterogeneous vehicle formations was developed. Subsequently, an interactive strategy was established for queueing under the coordination of connected human-driven and autonomous vehicles, and the queue formation, merging, and splitting processes are divided according to the cooperative interaction strategy. Finally, the proposed lane management and formation strategies are verified using the SUMO+Veins simulation software. The simulation results show that the dedicated lane for connected vehicles can effectively improve road traffic efficiency with a market penetration rate of 30%, but the lane performance declines when the market penetration rate reaches 45%, mainly manifested by a turning point in the average speed of vehicles. After setting up the dedicated lane, the overall traffic efficiency increases by 58.8% according to the simulation of vehicle formations driving on the dedicated lane at a market penetration rate of 50%.
Zhang, Xiqiao, Cui, Leqi, Yang, Longhai, Wang, Gang
Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure12-08-02-00202/24/2025
Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements on off-board perception systems, the communication network, and AV tactical safety behavior. The methodology is instantiated as a concrete set of models and a tool, exercised through a case study involving intersection traffic conflicts. We show how the age of information and observation uncertainty affect the collaborative system design space and further discuss the generalization and other findings from both the methodology and case study development.
Fornaro, Gianfilippo, Törngren, Martin, Gaspar Sánchez, José Manuel
Intelligent Vehicle Trajectory Tracking Control Based on Variable Universe Fuzzy Rule Speed Planning and Piecewise Preview Model Prediction02-18-01-00062/6/2025
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy rules are introduced to design the longitudinal speed planning method, in which the road friction coefficient and road curvature are defined as the input of the speed planning method, and the vehicle lateral deviation is defined as the scaling factor input of the speed variable universe. Based on the dynamics model and trajectory tracking model, the PPMPC method is proposed to improve the accuracy and stability of trajectory tracking. During the PPMPC method design, the reference value of state quantity in the prediction horizon can be updated by using further road information and planning longitudinal speed information. Finally, the results show that the proposed planning algorithm can provide a reasonable longitudinal speed to reduce the tracking lateral error in the tracking control, and the proposed PPMPC can significantly improve the vehicle speed-tracking accuracy and control stability compared with the traditional model predictive control (MPC) method.
Zhang, Jie, Teng, Shipeng, Gao, Jianjie, Zhou, Xingxing, Zhou, Junchao
Enhanced Adaptive Cruise Control Strategies Considering Front and Rear Vehicles10-09-01-00071/29/2025
Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a novel formulation based on vehicle kinematics to devise an optimal reference speed to maintain safe distance with both vehicles, which is fed to the lower level MPC that generates the corresponding throttle and brake values to track the reference speed while ensuring smooth speed transitions. Priority is given to the front vehicle in conflicting situations. Finally, the efficacy of the control strategies is validated using industry-standard simulation software Prescan by assessing the comparative performances of both the control strategies. The results of this study will provide valuable insights into the enhancement of ACC systems by improving the distance safety margin of the ACC-equipped vehicle with respect to the rear vehicle along with the front, ultimately contributing to better throughput of traffic and safer road mobilization.
Sharma, Vishrut, Sengupta, Somnath, Ghosh, Susenjit
Solutions for Making Group Travel More Connected and Easy2024-28-002910/17/2024
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.
Barre, Deva Harshitha
Cyber Security Challenges in V2C and in Vehicle Network2024-28-003510/17/2024
Based on advanced Automotive functionality, Vehicle networks has enabled the exchange of data to multiple domains and to meet these demands, more complex software applications, some of which require service-based cloud are developed. Exposure of data creates multiple threats for attacker to tamper security and privacy. Automotive cybersecurity topic has gained momentum based on multiple gaps identified in Automotive In vehicle and around the vehicle networks. In this paper, we provide an extensive overview on V2C (Vehicle to Cloud) and In-vehicle data protection, we also highlight methods to identify threats on any vehicle network connected to V2C and identify methods to verify security functionality using Fuzz or Penetration test protocol, we have identified gaps in existing security solutions and outline possible open issues and probable solution.
Panda, Jyotiprakash, Jain, Rushabh Deepakchand
On-Board System Requirements for V2V Safety Communications by Non-Light-Duty Vehicles and MotorcyclesJ2945/1B_202410 (Current)10/8/2024
This SAE Standard specifies the system requirements for vehicle-to-vehicle (V2V) safety system for Federal Highway Administration (FHWA) vehicle classes 1 (motorcycles) and 6 through 13 (non-light-duty vehicles), including functional requirements and performance requirements. The system can transmit and receive the SAE J2735-defined Basic Safety Message (BSM) over a wireless communications interface; the communications interface itself is outside the scope of this document. This document provides the specifications necessary to build interoperable systems that support V2V safety applications for non-light-duty vehicles, as well as motorcycles which rely on the exchange of BSMs. The document covers vehicle classes not addressed in SAE J2945/1 and SAE J3161/1.
V2X Core Technical Committee
On-Board System Requirements for V2V Safety Communications by School BusesJ2945/1C_202409 (Current)9/10/2024
This document specifies the on-board system requirements for vehicle-to-vehicle (V2V) safety system for school buses, including functional requirements and performance requirements. The system can send the SAE J2735-defined basic safety message (BSM) over a wireless communications interface; the communications interface itself is outside the scope of this document. This document provides the specifications necessary to build interoperable systems that support V2V applications that rely on receiving BSMs from school buses.
V2X Core Technical Committee
Platooning of Position-Controlled Vehicles on a Curved Path under Communication Losses and Delays12-07-04-00288/21/2024
This article offers an algorithmic solution for moving a homogeneous platoon of position-controlled vehicles on a curved path with varying speeds and in the presence of communication losses and delays. This article considers a trajectory-based platooning with the leader–following communication topology, where the lead vehicle communicates its reference position and orientation to each autonomous follower vehicle. A follower vehicle stores this communicated information for a specific period as a virtual trail of the lead vehicle starting from the lead vehicle’s initial position and orientation. An algorithm uses this trail to find the follower vehicle’s reference position and orientation on that trail, such that the follower vehicle maintains a constant distance from the lead vehicle. The proposed algorithm helps form a platoon where each vehicle can traverse a curve with varying speeds. In contrast, in the existing literature, most of the solutions for vehicle platooning on a curved path demonstrate constant speed motion. This article considers delayed information as lost. Therefore, the communication losses and delays are considered as packet drops. The algorithm uses estimates of the lost information of the lead vehicle’s trail to generate the reference path for the follower vehicles. Analysis of the algorithm shows a relationship between the platoon’s performance and the extent of packet drops. Simulation results agree with the relationship.
Bhaskar, Rintu, Wahi, Pankaj, Potluri, Ramprasad
Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory Models12-07-04-00256/13/2024
In the realm of transportation science, the advent of deep learning has propelled advancements in predicting longitudinal driving behavior. This study explores the application of deep neural network architectures, specifically long–short-term memory (LSTM) and convolutional neural networks (CNNs), recognized for their effectiveness in handling sequential data. Using a 3-s temporal window that includes past vehicle progress, speed, and acceleration, the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed and progress for the next 6 s. The approach achieves state-of-the-art performance, particularly within a 4 s horizon, but remains competitive even for longer-term predictions. This is achieved despite the simplicity of its input space, which does not include information about vehicles other than the target vehicle. As a result, while its performance may decrease slightly for longer-term predictions due to the lack of environmental information, it still offers reliable predictions and can be applied effectively in scenarios with partial observability. The comparative analysis of multilayer perceptron (MLP), LSTM, and one-dimensional CNN architectures highlights the challenges faced by MLP in capturing the complex nonlinearity of driving behavior. LSTM and CNN demonstrate superior performance, with model complexity influencing outcomes. No statistically significant difference is observed in the performance between LSTM and CNN models.
Lucente, Giovanni, Maarssoe, Mikkel Skov, Kahl, Iris, Schindler, Julian
Facial Recognition Software and Privacy Law in Transportation TechnologyEPR20240115/7/2024
Data privacy questions are particularly timely in the automotive industry as—now more than ever before—vehicles are collecting and sharing data at great speeds and quantities. Though connectivity and vehicle-to-vehicle technologies are perhaps the most obvious, smart city infrastructure, maintenance, and infotainment systems are also relevant in the data privacy law discourse. Facial Recognition Software and Privacy Law in Transportation Technology considers the current legal landscape of privacy law and the unanswered questions that have surfaced in recent years. A survey of the limited recent federal case law and statutory law, as well as examples of comprehensive state data privacy laws, is included. Perhaps most importantly, this report simplifies the balancing act that manufacturers and consumers are performing by complying with data privacy laws, sharing enough data to maximize safety and convenience, and protecting personal information. Click here to access the full SAE EDGETM Research Report portfolio.
Eastman, Brittany
Vehicle Experience and Human Interaction: Vol. 1EPRCOMPV1720235/3/2024
Connected and autonomous vehicles (CAVs) and their productization are a major focus of the automotive and mobility industries as a whole. However, despite significant investments in this technology, CAVs are still at risk of collisions, particularly in unforeseen circumstances or “edge cases.” It is also critical to ensure that redundant environmental data are available to provide additional information for the autonomous driving software stack in case of emergencies. Additionally, vehicle-to-everything (V2X) technologies can be included in discussions on safer autonomous driving design. Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including CAVs. It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The benefits of R2V are not limited to fully autonomous vehicles (e.g., SAE Level 4), but can also be used in Level 2 CAV scenarios. However, despite the potential benefits of R2V, discussions on this topic are still limited. This chapter aims to provide an overview of R2V technology and its applications for CAV systems, particularly in the context of collision-avoidance features. The responder vehicles in question can be autonomous or non-autonomous. It is hoped that it will provide valuable information and knowledge on vehicle connectivity and automation in the current automotive and mobility ecosystem, enabling the development of safer and more reliable autonomous driving technology. The chapter is intended for both industrial and academic experts and is expected to stimulate further discussions on the development and standardization of R2V technology.
Abdul Hamid, Umar Zakir, Roth, Christian, Nickerson, Jeffrey, Lyytinen, Kalle, King, John Leslie
Modelling and Analysis of a Cooperative Adaptive Cruise Control (CACC) Algorithm for Fuel Economy2024-01-25644/9/2024
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration and speed, with each other. Using sensors such as radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, a CACC system is presented, where the acceleration of the lead vehicle is used in the calculation of desired vehicle speed. In addition to the smooth car following abilities, the proposed CACC also has the capability to calculate a speed profile for the ego vehicle that is fuel efficient, making it an Ecological CACC (Eco-CACC) model. Simulations were run to model and test the Eco-CACC algorithms with different lead vehicle driving behaviors. The performance of the new Eco-CACC model is then compared to a Proportional Derivative (PD) based Adaptive Cruise Control (ACC) system that aimed to follow the lead vehicle as closely as possible. The PD controller was tuned for nominal performance. The preliminary results show that the proposed CACC model was able to decrease the rate of acceleration and decelerations experienced by the ego vehicle to attain a smooth speed profile that consumed less fuel than its PD-controlled ACC counterpart.
Kavas-Torris, Ozgenur, Guvenc, Levent
Developing a dynamic test protocol for the updated IIHS deformable barrier2024-01-25104/9/2024
The Insurance Institute for Highway Safety (IIHS) introduced its updated side-impact ratings test in 2020 to address the nearly 5,000 fatalities occurring annually on U.S. roads in side crashes. Research for the updated test indicated the most promising avenue to address the remaining real-world injuries was a higher severity vehicle-to-vehicle test using a striking barrier that represents a sport utility vehicle. A multi-stiffness aluminum honeycomb barrier was developed to match these conditions. The complexity of a multi-stiffness barrier design warranted research into developing a new dynamic certification procedure. A dynamic test procedure was created to ensure product consistency. The current study outlines the process to develop a dynamic barrier certification protocol. The final configuration includes a rigid inverted T-shaped fixture mounted to a load cell wall. This fixture is impacted by the updated IIHS moving deformable barrier at 30 km/h. The fixture represents the stiff sections of a typical vehicle’s side structure and creates deformation patterns on the deformable barrier that are similar to the baseline results established during the development of the updated IIHS side crashworthiness test. Three different barrier manufacturers submitted 3–4 samples each for evaluation. These barriers were tested to determine performance and quality across manufacturers. Load cell wall data was used to establish force-displacement corridors for the center and left/right sections of the deformable barrier. The corridors will be used as part of a periodic certification process that barrier manufacturers can use to prove their finished products and manufacturing processes meet IIHS requirements.
Mueller, Becky, Arbelaez, Raul, Heitkamp, Eric, Mampe, Christopher
Data-Driven Estimation of Coastdown Road Load2024-01-22764/9/2024
Emissions and fuel economy certification testing for vehicles is carried out on a chassis dynamometer using standard test procedures. The vehicle coastdown method (SAE J2263) used to experimentally measure the road load of a vehicle for certification testing is a time-consuming procedure considering the high number of distinct variants of a vehicle family produced by an automaker today. Moreover, test-to-test repeatability is compromised by environmental conditions: wind, pressure, temperature, track surface condition, etc., while vehicle shape, driveline type, transmission type, etc. are some factors that lead to vehicle-to-vehicle variation. Controlled lab tests are employed to determine individual road load components: tire rolling resistance (SAE J2452), aerodynamic drag (wind tunnels), and driveline parasitic loss (dynamometer in a driveline friction measurement lab). These individual components are added to obtain a road load model to be applied on a chassis dynamometer. However, lab-tested quantities may not account for environmental noise factors and qualitative vehicle characteristics leading to a significant residual road load between the track-tested and lab-tested road loads. Regression modeling techniques are explored for estimating this residual road load and the challenges are discussed. Additionally, a technique is developed to choose feature selection metrics using simulation of multivariate non-gaussian continuous and discrete data having similar statistical properties as the data obtained from automotive road tests. Using the selected features, two regularized regression techniques are experimented with. The first technique models the residual road load power, while the second technique models a polynomial relationship between vehicle speed and residual road load.
Singh, Yuvraj, Jayakumar, Adithya, Rizzoni, Giorgio
Design, Prototyping, and Implementation of a Vehicle-to-Infrastructure (V2I) System for Eco-Approach and Departure through Connected and Smart Corridors2024-01-19824/9/2024
The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient automated navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S. Department of Energy, MathWorks, and General Motors, where each team received a 2019 Chevrolet Blazer from General Motors and was tasked with achieving SAE Level 2 automation to increase the vehicle’s energy efficiency, performance, and connectivity, among other features. Specifically, teams were challenged to add V2I connectivity, requiring the ability to transmit Basic Safety Messages (BSMs) with real-time vehicle information and receive BSMs from other vehicles and SPaT/MAP (Signal Phase and Timing/Map Data) data from Roadside Units (RSUs). This integration of V2X messages (BSM/SPaT and MAP) in cooperative driving systems enhances overall road safety by providing real-time, detailed information about the conditions and intentions of vehicles, fostering a more secure and efficient transportation ecosystem. The Ohio State University’s EcoCAR’s Connected and Automated Vehicles (CAVs) Sub team was able to implement V2I technology successfully using Cohda Wireless MK5 DSRC (Dedicated Short Range Communication) On-Board Unit (OBU) connected with a Mobilemark MGW-303 antenna, combining two 5.9GHz antennas for DSRC with an active GNSS antenna.
Chowduri, Suhrit, Midlam-Mohler, Shawn, Singh, Karun Prateek
Cooperative Driving Automation (CDA) Feature - Infrastructure-Based Prescriptive Cooperative MergeJ3256_202403 (Current)3/4/2024
This SAE Information Report describes a concept of operations (CONOPS) for a Cooperative Driving Automation (CDA) Feature for infrastructure-based prescriptive cooperative merge. This work focuses on a Class D (Prescriptive; refer to J3216) CDA infrastructure-based cooperative merge Feature, supported by Class A (Status-Sharing) or Class C (Agreement-Seeking) messages among the merging cooperative automated driving system-operated vehicles (C-ADS-equipped vehicles). This document also provides a test procedure to evaluate this CDA Feature, which is suitable for proof-of-concept testing in both virtual and test track settings.
Cooperative Driving Automation(CDA) Committee
Letter from the Focus Issue Editors14-13-01-00012/2/2024
Letter from the Focus Issue Editors
Song, Ziyou, Feng, Shuo, Wu, Guoyuan, Li, Zhaojian
Vehicle Diagnostics and Vehicle to Infrastructure Communication through Visible Light Communication (VLC)2024-26-00821/16/2024
High Fidelity Communication has become a necessity in various sectors. Different wireless data transfer methods play a vital role in various far field and near-field communications. Wireless communication for transferring data through radio spectrum has been a continuous evolving trend, especially in Automotive Sector, with fleet monitoring, platooning and even connected vehicles. Some important parameters considered in selecting a wireless platform would be bandwidth, data transfer, speed and security. Some interesting advantages of communication over the visible spectrum has led to the evolution of Light Fidelity. Implementation of Visible Light Communication (VLC) in the automotive field might enable safer driving conditions through vehicle-to-vehicle (V2V) and vehicle to Infrastructure (V2I) communication with high data transmission rates and efficient-bandwidth usage. The principle of VLC is based on “line of sight” data transmission through modulation of the light source. Highly reliable vehicle-to-vehicle communication is possible with VLC through transmission and reception of data using visible light as the medium of communication. It also helps to reduce the downtime with on the fly diagnosis of the vehicle. In this paper, we propose a concept that uses VLC enabled vehicles and systems aimed at improved vehicle diagnostics, data handling and road safety. We shall also investigate possibilities of managing and reporting traffic violations accurately by means of deploying the VLC technology in real time applications. The paper also touches upon VLC in hybrid networks wherein it collaborates with the conventional networks to enable wireless communication across various scenarios and environments. Also, this paper would focus on the vehicle diagnostics through ECUs for faster data handling.
Ali, Rifat Fahmida, N, Prem, Natarajan, Akshay, Kashi, Anitha
Lend a Range: Vehicle-to-Vehicle (V2V) Charging2024-26-01071/16/2024
With increase in number of EVs on Indian roads, poised EV makers to produce innovative and pragmatic concept of electric vehicle features. The concept of bidirectional charging is one of that and which is creating buzz and curiosity among EV buyers. The bidirectional charging enables EV owners to lend the power to grid, other vehicles or use for other auxiliary applications. This paper focuses on idea of vehicle-to-vehicle (V2V) level 1, level 2 AC charging using J1772 standard, and level 3 DC fast charging using ISO 15118 or DIN 70121. where one user can lend a range of few kilometers to other based on requirement as a helping hand. This paper proposes a new idea which enable vehicle-to-vehicle (V2V) charging using ISO 15118, DIN70121 and J1772 protocol. In V2V charging, source vehicle shall function as a mobile charging source (EVSE) and other shall function as a sink (EV). The idea of making source vehicle as charging station involves sink vehicle authentication and managing the power flow. This paper discusses the overall system level architecture involving need of mobile or remote charging, sources vehicle willingness, V2V authentication, charging request, power flow control, and charging execution.
Kumar, Rohit, Penta, Amar, Venugopal, Karthick Babu, Sahu, Hemant, Arya, Harshita
An Energy-Efficient Merge-Aware Cruise Control Method for Connected Electric Vehicles14-13-01-001012/28/2023
This article presents a merge-aware cruise control method that incorporates vehicle-to-vehicle (V2V) information and aims at improving the energy efficiency of vehicles and reducing speed disruptions of merging traffic during highway merges. During the events of highway merges, the gap between the ego and the preceding vehicle reduces drastically, which can result in sudden braking of the ego vehicle and thus reduction of its energy efficiency. We propose a rather simple cruise control algorithm to eliminate such sudden variations in the gap and velocity with respect to the preceding vehicle during highway merges, thus reducing the large accelerations and braking during such events and thereby improving energy efficiency. The proposed algorithm incorporates future traffic information and has computational requirements similar to adaptive cruise control methods, hence it is real-time applicable. Data used in this article are taken from on-road experiments using a 2020 Tesla Model 3. Simulation results show the efficacy of our proposed control algorithm.
Vellamattathil Baby, Tinu, HomChaudhuri , Baisravan
Cooperative Lane Change Control Based on Null-Space-Behavior for a Dual-Column Intelligent Vehicle Platoon2023-01-706412/20/2023
With the extension of intelligent vehicles from individual intelligence to group intelligence, intelligent vehicle platoons on intercity highways are important for saving transportation costs, improving transportation efficiency and road utilization, ensuring traffic safety, and utilizing local traffic intelligence [1]. However, there are several problems associated with vehicle platoons including complicated vehicle driving conditions in or between platoon columns, a high degree of mutual influence, dynamic optimization of the platoon, and difficulty in the cooperative control of lane change. Aiming at the dual-column intelligent vehicle platoon control (where “dual-column” refers to the vehicle platoon driving mode formed by multiple vehicles traveling in parallel on two adjacent lanes), a multi-agent model as well as a cooperative control method for lane change based on null space behavior (NSB) for unmanned platoon vehicles are established in this paper. Specifically, a multi-agent model of the dual-column vehicle platoon is first established, which adopted a dual-star communication architecture based on “vehicle-to-vehicle” interactions. Then, rules for changing lanes between platoons are designed, and a method based on the risk perception coefficient for determining the priority of the task is developed. Finally, a cooperative control method of lane change based on NSB is proposed to further resolve the conflict between the lane change task and the collision avoidance task. The cooperative control method based on NSB is validated under the condition of sudden deceleration during the lane change task using a driving simulator. Validation results demonstrate that the method can ensure the safety of the platoon and implement cooperative lane change between the platoon columns stably and efficiently.
Yan, Danshu, Zhao, Zhiguo, Liang, Kaichong, Yu, Qin
A Tutorial on V2I Communication: Evaluating the LTE-V2X for Day-1 V2I and V2V Integration in Congested Scenarios12-06-04-002711/29/2023
Because of the growing interest in LTE-V2X, there is a need to describe its performance under various conditions and scenarios. This article explores the deployment of long-term evolution vehicle-to-everything (LTE-V2X) technology for vehicle-to-infrastructure (V2I) communication and delves into the deployment of LTE-V2X communication in three major global regions: the United States, Europe, and China. We begin with an overview of the functionality of LTE-V2X and highlight the objectives of V2I communication in terms of safety and mobility applications—and describe why it will be the predominant type of V2X in the first few years of deployment. We also examine the specific Day-1 V2I message sets standardized in each region, along with their potential applications and benefits. The technical details and use cases using these messages are discussed, along with the benefits they offer in improving the accuracy, reliability, and safety for surface transportation. Additionally, our field trial and simulation results demonstrate why, from a technical basis, LTEV2X is the technology of choice in North America and China. In particular, the performance of V2I and vehicle-to-vehicle (V2V) communication in a single 20 MHz channel (Channel 183) is highlighted. The results show that LTE-V2X technology can support both V2V and V2I communication and that Day-1 V2I messages can be effectively transmitted and received in a congested urban environment. Overall, we highlight the potential of LTE-V2X technology for V2V and V2I communication in enabling safety and efficiency in the current and next generation of transportation systems.
Hajisami, Abolfazl, Weber, Ralf, Misener, Jim, Chetlur, Vishnu Vardhan, Ruder, Michael, Chen, Shuping
On-Board System Requirements for V2V Safety Communications by Non-Light-Duty Vehicles and MotorcyclesJ2945/1B_202311 (Historical)11/27/2023
This SAE Standard specifies the system requirements for vehicle-to-vehicle (V2V) safety system for FHWA vehicle classes 1 (motorcycles and 4 through 13 (non-light-duty), including functional requirements and performance requirements. The system can transmit and receive the SAE J2735-defined Basic Safety Message (BSM) over a wireless communications interface; the communications interface itself is outside the scope of this document. This document provides the specifications necessary to build interoperable systems that support V2V safety applications for non-lightweight vehicles, as well as motorcycles which rely on the exchange of BSMs. The document covers vehicle classes not addressed in SAE J2945/1 and SAE J3161/1.
V2X Core Technical Committee
Letter from the Special Issue Editor12-06-04-002411/21/2023
Letter from the Special Issue Editor
Riehl, Jonathan
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