Browse Topic: Cooperative driving automation

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A Simple Analytical Model for Predicting Cooperative Ratio in Agreement-Seeking Cooperative Driving Automation with C-V2X Communication2026-01-0078To be published on 04/07/2026
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
Zhan, Lu, Hyeon, Eunjeong, JEONG, Jongryeol, Misra, Priyash, Di Russo, Miriam, Das, DEBASHIS, Stutenberg, Kevin
Quantifying Highway Flow Improvements via CACC-Based Truck Platooning: A Statistical Modeling Approach2026-01-0463To be published on 04/07/2026
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 in terms of the effectiveness in improving highway mobility under varying traffic conditions. A statistical regression model is developed and calibrated using simulation of real-world highway networks to identify the key factors and assess the flow improvement of truck platoons on highways. The analysis focuses on five primary variables: desired platoon speed, platoon size, space headway, platooning truck percentage, and non-platoon traffic flows. The study systematically evaluates how each of these
Karbasi, Amir Hossein, Wang, Jinghui, Yang, Hao
Characterizing the Impact of C-V2X PC5 Radio on Cooperative Driving Automation in Hardware-in-the-Loop Experiments2026-01-0077To be published on 04/07/2026
The SAE J3216 standard defines Cooperative Driving Automation (CDA), which has gained 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. However, limited research has investigated how Cellular Vehicle-to-Everything (C-V2X) technology affects CDA applications, especially the agreement seeking operations. This work presents a hardware-in-the-loop (HIL) experimental study designed to evaluate the Argonne CDA controller under varying C-V2X radio transmission frequencies and message configurations. A three-vehicle car-following scenario was implemented in the Argonne-developed Roadrunner Simulator, incorporating CDA agreement-seeking operations, vehicle powertrain models, and V2V communication modules. The CDA messages were then exchanged through two actual C-V2X radios with realistic communication impairment caused by the hardware
Zhan, Lu, Hyeon, Eunjeong, Misra, Priyash, JEONG, Jongryeol, Di Russo, Miriam, Das, DEBASHIS, Stutenberg, Kevin
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
Pan, Deng, Yao, Zhiyong, Wang, Chang, Ji, Jie, Zhang, Bohan
Commercial Vehicles: Vol. 2EPRCOMPV05202512/29/2025
Hsu, Tsung-Ming, Muelaner, Jody Emlyn
Enhanced Cooperative Perception through Asynchronous Vehicle-to-Infrastructure Framework with Delay Mitigation for Connected and Automated Vehicles12-09-01-00088/8/2025
Perception is a key component of automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle-to-infrastructure (V2I) technology promises to enhance scene representation for connected and automated vehicles (CAVs) in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR, or sensor fusion methods, such as LIDAR–camera and RADAR–camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits their ability to capture detailed object
Saravanan, Nithish Kumar, Jammula, Varun Chandra, Yang, Yezhou, Wishart, Jeffrey, Zhao, Junfeng
DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking2025-01-02947/2/2025
We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in
Beutenmüller, Frank, Brostek, Lukas, Doberstein, Christian, Han, Longfei, Kefferpütz, Klaus, Obstbaum, Martin, Pawlowski, Antonia, Rössert, Christian, Sas-Brunschier, Lucas, Schön, Thilo, Sichermann, Jörg
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
Cooperative Driving Automation(CDA) Committee
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
Sharma, Vishrut, Sengupta, Somnath, Ghosh, Susenjit
A Reinforcement Learning-Based Parameter Tuning Approach for a Secure Cooperative Adaptive Cruise Control System12-08-04-00331/25/2025
Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the
Javidi-Niroumand, Farahnaz, Sargolzaei, Arman
Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems2024-01-702812/13/2024
Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructure to address the occlusion and sensing range limitation issues. To date, existing research is mainly focused on prototyping cooperative perception using only one type of sensor such as LiDAR and camera. In such cases, the performance of cooperative perception is constrained by individual sensor limitations. To exploit the multi-modality of sensors to further improve distant object detection accuracy, in this paper, we propose a unified multi-modal multi-agent cooperative perception framework that integrates camera and LiDAR data to enhance perception performance in intelligent transportation systems. By leveraging the complementary strengths of LiDAR and camera sensors, our framework utilizes the geometry information from LiDAR and the semantic information from cameras to achieve an accurate cooperative perception
Meng, Zonglin, Xia, Xin, Zheng, Zhaoliang, Gao, Letian, Liu, Wei, Zhu, Jiaqi, Ma, Jiaqi
CPTPS: A Cyber-Physical Trajectory Planning System for Trajectory Planning of Autonomous Vehicles in Unstructured Space2024-01-702511/15/2024
Internet of vehicles (IoV) system as a typical application scenario of smart city, trajectory planning is one of the key technologies of the system. However, there are some unstructured spaces such as road shoulders and slopes pose challenges for trajectory planning of connected-automated vehicle (CAV). Therefore, this paper addresses the problem of CAV trajectory planning affected by unstructured space. Firstly, based on cyber-physical system (CPS), the cyber-physical trajectory planning system (CPTPS) framework was built. A high-precision digital twin CAV is established based on the physical properties and geometric constraints of CAV, and the digital model is mapped to cyber space of the CPTPS. In order to further reduce the energy consumption of the CAV during driving and the time spent from the start to the end, a model was established. Further, based on the sand cat swarm hybrid particle swarm optimization algorithm (SCSHPSO), global path planning for connected-automated vehicles
Ma, Shizi, Ma, Zhitao, Shi, Ying, Yang, Zhongkai, Lai, Daoyin, Qi, Zhiguo
Rating the Surrounding Vehicle-to-Everything Field, Based on Channel Utilization and Information Influence12-08-03-002510/22/2024
Automated vehicles (AVs) can get additional information from infrastructure and other vehicles via vehicle-to-everything (V2X) communication. However, how can an AV decide if the surrounding V2X field can reliably provide qualitative, relevant, and trustworthy information? Related research analyzes V2X performance from various angles. However, not only are there identified open gaps in the analysis of loaded channels, but there has also not yet been an effort to design a lightweight metric for rating the quality of the surrounding V2X field. Hence, this work aims to close this existing performance measurement gap and develop a metric for rating the quality of the surrounding V2X field. This article first highlights the gaps identified in performance analysis before closing them with a dedicated measurement campaign. Next, it combines these findings with related research to design a straightforward V2X field rating metric. The resulting V2X field rating metric is a starting point for
Pilz, Christoph, Kuschnig, Lukas, Steinberger, Alina, Sammer, Peter, Piri, Esa, Couturier, Christophe, Neumayr, Thomas, Schratter, Markus, Steinbauer-Wagner, Gerald
A Novel Resource-Aware Distributed Cooperative Decision-Making Mechanism for Connected Automated Vehicles12-07-04-00309/6/2024
This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as
Ghahremaninejad, Reza, Bilgen, Semih
A Methodology to Enhance Transparency for Trustworthy Artificial Intelligence for Cooperative, Connected, and Automated Mobility12-08-01-00109/2/2024
In this research, we propose a set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications. By analyzing key documents on ethical guidelines and regulations in AI, such as the Assessment List for Trustworthy AI and the EU AI Act, we extracted considerations regarding transparency requirements. Recognizing the unique characteristics of each AI system and its application sector, we designed a model card tailored for CCAM applications. This was made considering the criteria for achieving trustworthy autonomous vehicles, exposed by the Joint Research Centre (JRC), and including information items that evidence the compliance of the AI system with these ethical aspects and that are also of interest to the different stakeholders. Additionally, we propose an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system.
Cañas, Paola Natalia, Nieto, Marcos, Otaegui, Oihana, Rodriguez, Igor
Cooperative Infrastructure CDA Feature: Cooperative Permissive Left Turn Across Opposing Traffic with Infrastructure GuidanceJ3282_202406 (Current)6/26/2024
This report provides a concept of operations needed to evaluate a CDA Feature for a permissive left turn across opposing traffic, with infrastructure guidance. The Feature uses CDA cooperation levels including status-sharing and agreement-seeking, and a set of test scenarios (functional, logical, and concrete) is developed to evaluate this CDA Feature.
Cooperative Driving Automation(CDA) Committee
Decentralized Control for CACC Systems Accounting for Uncertainties2024-37-00106/12/2024
Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized model predictive control (MPC) approach that incorporates Kalman filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB/Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a reinforcement learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability.
Seifoddini, Arash, Azad, Arefeh, Musa, Alessia, Misul, Daniela
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
Kavas-Torris, Ozgenur, Guvenc, Levent
A Guidance Strategy for Parking Space of Autonomous Valet Parking with Cooperative Vehicle Infrastructure System2024-01-25714/9/2024
Lots of invalid volume of traffic occurs as the vehicle repeatedly seeking the valid berth location, due to the existence of information islands, which reduces traffic efficiency, increases traffic congestion and the emission of pollutants. Aiming at eliminating the existence of information islands, this paper proposed a guidance strategy for parking space of autonomous valet parking, as taking merits of cooperative vehicle infrastructure system. The guidance strategy consists of an optimal guidance model and an adaptive ant-colony algorithm. Firstly, the optimal guidance model takes the minimum total parking cost as the objective function and the capacity of parking space as the constraints. Both the objective costs of parking and the subjective cost of customer are taken into accounts in the objective function. Secondly, comparing with traditional method, the adaptive ant-colony algorithm taking two improvements, in order to accelerate the convergence of the algorithm and avoid
Zhang, Zhouping, Liu, Weidong, Sun, Zhipeng, Zhu, Zuwei, Hu, Yiming, Zeng, Dequan
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
Performance Analysis of Cooperative Truck Platooning under Commercial Operation during Canadian Winter Season12-07-02-001211/14/2023
The cooperative platoon of multiple trucks with definite proximity has the potential to enhance traffic safety, improve roadway capacity, and reduce fuel consumption of the platoon. To investigate the truck platooning performance in a real-world environment, two Peterbilt class-8 trucks equipped with cooperative truck platooning systems (CTPS) were deployed to conduct the first-of-its-kind on-road commercial trial in Canada. A total of 41 CTPS trips were carried out on Alberta Highway 2 between Calgary and Edmonton during the winter season in 2022, 25 of which were platooning trips with 3 to 5 sec time gaps. The platooning trips were performed at ambient temperatures from −24 to 8°C, and the total truck weights ranged from 16 to 39 tons. The experimental results show that the average time gap error was 0.8 sec for all the platooning trips, and the trips with the commanded time gap of 5 sec generally had the highest variations. The average number of disengagements increased when the
Jiang, Luo, Kheyrollahi, Javad, Koch, Charles Robert, Shahbakhti, Mahdi
Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ ASN FileJ2945/6A_202310 (Current)10/26/2023
This Abstract Syntax Notation (ASN.1) file precisely specifies the structure of the data used to support the implementation of SAE International Standard 2945/6. Using the ASN.1 specification, a compiler tool can be used to produce encodings as required by the encoding rules identified in the standard. Both this file and the SAE J2735 ASN.1 files are necessary to collectively implement the data exchange described in the J2945/6. The combined library can be used by any application (along with the additional logic of the application) to exchange the data over an interface conformant to J2945/6. SAE J2945/6 specifies the interface and requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning. It provides the information necessary to build interoperable systems that support CACC, which rely on the exchange of Cooperative Control Messages. You may also be interested in: Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ the J2945/6 PDF
V2X Core Technical Committee
Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ SET FileJ2945/6S_202310 (Current)10/26/2023
This Abstract Syntax Notation (ASN.1) file precisely specifies the structure of the data used to support the implementation of SAE International Standard 2945/6. Using the ASN.1 specification, a compiler tool can be used to produce encodings as required by the encoding rules identified in the standard. Both this file and the SAE J2735 ASN.1 files are necessary to collectively implement the data exchange described in the J2945/6. The combined library can be used by any application (along with the additional logic of the application) to exchange the data over an interface conformant to J2945/6. SAE J2945/6 specifies the interface and requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning. It provides the information necessary to build interoperable systems that support CACC, which rely on the exchange of Cooperative Control Messages. You may also be interested in: Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ the J2945/6 PDF
V2X Core Technical Committee
Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and PlatooningJ2945/6_202310 (Current)10/26/2023
This standard provides the guideline for enhancements to adaptive cruise control (ACC) by the addition of wireless communication from relevant vehicles (V2V) and/or the infrastructure (I2V) to augment the ACC active sensing capability. The CACC system operates under driver responsibility and supervision and is limited to the following: Does only longitudinal control of the vehicle. Uses time gap control strategy similar to ACC. Motor vehicles covered in the scope of this document include light and heavy vehicles. The message elements to realize CACC and platooning are part of the scope. The initial release covers definitions for CACC and platooning and requirements for CACC, while a subsequent release will cover the platooning requirements.
V2X Core Technical Committee
Robust Multiagent Reinforcement Learning toward Coordinated Decision-Making of Automated Vehicles10-07-04-00319/4/2023
Automated driving is essential for developing and deploying intelligent transportation systems. However, unavoidable sensor noises or perception errors may cause an automated vehicle to adopt suboptimal driving policies or even lead to catastrophic failures. Additionally, the automated driving longitudinal and lateral decision-making behaviors (e.g., driving speed and lane changing decisions) are coupled, that is, when one of them is perturbed by unknown external disturbances, it causes changes or even performance degradation in the other. The presence of both challenges significantly curtails the potential of automated driving. Here, to coordinate the longitudinal and lateral driving decisions of an automated vehicle while ensuring policy robustness against observational uncertainties, we propose a novel robust coordinated decision-making technique via robust multiagent reinforcement learning. Specifically, the automated driving longitudinal and lateral decisions under observational
He, Xiangkun, Chen, Hao, Lv, Chen
Cooperative Driving Automation (CDA) Feature: Perception Status Sharing for Occluded Pedestrian Collision AvoidanceJ3251_202308 (Current)8/22/2023
This SAE Information Report develops a concept of operations (ConOps) to evaluate a cooperative driving automation (CDA) Feature for occluded pedestrian collision avoidance using perception status sharing. It 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
PHYSICALLY COOPERATING AUTONOMOUS GROUND VEHICLES2024-01-40598/10/2023
ABSTRACT Off-road mobility for an individual autonomous ground vehicle (AGV) can be severely limited by extreme environments (such as muddy patches or steep cliffs in off-road terrain). However, when operating as a group, cooperation between the AGVs can be leveraged to overcome such limitations. Traditionally cooperation has been achieved through information sharing, enabling the AGVs to “avoid” the extreme environments. In this paper we propose to achieve such cooperation through physical energy sharing, where the AGVs can “recover” from these environment scenarios. Specifically, we propose the use of a robotic manipulator (RM) that connects a disabled or degraded AGV with an operational AGV. A fleet level controller is proposed. The AGVs and the RM are modeled in Modelica, and integrated with the controller to perform simulations. We demonstrate collaborative movement in two scenarios, namely crossing a muddy patch and climbing a steep cliff. In each scenario the individual vehicle
Ashley, Michiel, McMullan, Davis, Gopalswamy, Swaminathan
Process for Developing an Interoperable Cooperative Driving Use Case Test Framework and Test ProceduresJ3252_202308 (Current)8/1/2023
This report provides the process for developing a flexible test framework to support the creation of system-level cooperative driving automation (CDA) Feature test procedures, which are intended to be objective, repeatable, and transparent, and enable collaborative testing of the Feature. Utilizing a Feature’s functional and logical scenario details, it provides the building blocks necessary to develop cooperative automated driving system (C-ADS)-equipped vehicle (C-ADS-V) and CDA infrastructure (CDA-I) system diagrams, identify the interfaces to and from the systems, and identify the set of functional test support components specific to the CDA Feature. Utilizing these details, along with the Feature-specific concrete scenarios, a method for developing a test scope and system level use-case-focused test procedures is provided.
Cooperative Driving Automation(CDA) Committee
MPC-Based Cooperative Longitudinal Control for Vehicle Strings in a Realistic Driving Environment2023-01-06894/11/2023
This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency. To keep our controller as close as possible to a real
Musa, Alessia, Miretti, Federico, Misul, Daniela
Application Protocol and Requirements for Maneuver Sharing and Coordinating ServiceJ3186_202303 (Current)3/23/2023
This SAE Standard provides requirements to support applications for the maneuver sharing and coordinating service (MSCS) beyond broadcast of basic safety messages (BSMs). This is to improve road safety and traffic efficiency by sharing and coordinating vehicle maneuvers via vehicle-to-everything (V2X) communications. This document lays out use case scenarios and defines vehicle-to-vehicle (V2V) application protocols, system requirements and message sets for MSCS. The defined message sets for MSCS will result in identifying new message types, data frames, and data elements for SAE J2735.
V2X Vehicular Applications Technical Committee
Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic12-05-04-00254/21/2022
Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) that require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp
Zhao, Xuanpeng, Liao, Xishun, Wang, Ziran, Wu, Guoyuan, Barth, Matthew, Han, Kyungtae, Tiwari, Prashant
Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed TrafficSAE-PP-0020611/13/2021
Modeling, prediction, and evaluation of personalized driving behaviors are crucial to emerging advanced driver-assistance systems (ADAS) which require a large amount of customized driving data. However, collecting such type of data from the real world could be very costly and sometimes unrealistic. To address this need, several high-definition game engine-based simulators have been developed. Furthermore, the computational load for cooperative automated driving systems (CADS) with a decent size may be much beyond the capability of a standalone (edge) computer. To address all these concerns, in this study we develop a co-simulation platform integrating Unity, Simulation of Urban MObility (SUMO), and Amazon Web Services (AWS), where Unity provides realistic driving experience and simulates on-board sensors; SUMO models realistic traffic dynamics; and AWS provides serverless cloud computing power and personalized data storage. To evaluate this platform, we select cooperative on-ramp
Wang, Ziran, Zhao, Xuanpeng, Liao, Xishun, Wu, Guoyuan, Barth, Matthew, Han, Kyungtae, Tiwari, Prashant
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202107 (Historical)7/16/2021
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the 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
Cooperative Driving Automation(CDA) Committee
Using Demanded Power and RDE Aggressiveness Metrics to Analyze the Impact of CACC Aggressiveness on Heavy Duty Platooning Power Consumption2021-01-00694/6/2021
Presently, a main mobility sector objective is to reduce its impact on the global greenhouse gas emissions. While there are many techniques being explored, a promising approach to improve fuel economy is to reduce the required energy by using slipstream effects. This study analyzes the demanded engine power and mechanical energy used by heavy-duty trucks during platooning and non-platooning operation to determine the aerodynamic benefits of the slipstream. A series of platooning tests utilizing class 8 semi-trucks platooning via Cooperative Adaptive Cruise Control (CACC) are performed. Comparing the demanded engine power and mechanical energy used reveals the benefits of platooning on the aerodynamic drag while disregarding any potential negative side effects on the engine. However, energy savings were lower than expected in some cases. It was hypothesized that the CACC may have amplified transient platooning events relative to the individual truck baseline results, hampering the
Siefert, Jan, Stegner, Evan, Snitzer, Philip, Ward, Jacob, Bevly, David M., Hoffman, Mark, Kotz, Andrew
SEMANTIC DIGITAL SURFACE MAP TOWARDS COLLABORATIVE OFF-ROAD VEHICLE AUTONOMY2024-01-38778/11/2020
ABSTRACT The fundamental aspect of unmanned ground vehicle (UGV) navigation, especially over off-road environments, are representations of terrain describing geometry, types, and traversability. One of the typical representations of the environment is digital surface models (DSMs) which efficiently encode geometric information. In this research, we propose a collaborative approach for UGV navigation through unmanned aerial vehicle (UAV) mapping to create semantic DSMs, by leveraging the UAV wide field of view and nadir perspective for map surveying. Semantic segmentation models for terrain recognition are affected by sensing modality as well as dataset availability. We explored and developed semantic segmentation deep convolutional neural networks (CNN) models to construct semantic DSMs. We further conducted a thorough quantitative and qualitative analysis regarding image modalities (between RGB, RGB+DSM and RG+DSM) and dataset availability effects on the performance of segmentation
Brand, Howard J. J., Li, Bing
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202005 (Historical)5/7/2020
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the 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
Cooperative Driving Automation(CDA) Committee
Analysis of On-Road Highway Testing for a Two Truck Cooperative Adaptive Cruise Control (CACC) Platoon2020-01-50093/27/2020
A Cooperative Adaptive Cruise Control (CACC) platooning system was developed and implemented on Class 8 heavy duty trucks. The system allows for longitudinal, or gap spacing, control of the vehicle, while lateral control is maintained by the driver. Many previous aerodynamic studies have shown a reduction in drag force from vehicles traveling in close proximity to each other. This “drafting” effect leads to potential fuel savings for all vehicles in the platoon. Several automated driving and CACC systems have been tested in simulation or closed track settings to evaluate these fuel savings. However, there are only a few examples of potential fuel savings in real on-road or highway environments. This paper provides control performance, fuel economy, lateral offset, and number of neighboring vehicle results of an on-road platoon. The CACC system was implemented on two Peterbilt 579 commercial trucks with unloaded 53’ box trailers. Testing occurred on highways around Montreal, Quebec with
Smith, Patrick, Bevly, David
Cooperative Adaptive Cruise Control (CACC) in Controlled and Real-World Environments: Testing and Results2024-01-38258/13/2019
ABSTRACT The transportation industry annually travels more than 6 times as many miles as passenger vehicles [1]. The fuel cost associated with this represents 38% of the total marginal operating cost for this industry [8]. As a result, industry’s interest in applications of autonomy have grown. One application of this technology is Cooperative Adaptive Cruise Control (CACC) using Dedicated Short-Range Communications (DSRC). Auburn University outfitted four class 8 vehicles, two Peterbilt 579’s and two M915’s, with a basic hardware suite, and software library to enable level 1 autonomy. These algorithms were tested in controlled environments, such as the American Center for Mobility (ACM), and on public roads, such as highway 280 in Alabama, and Interstates 275/696 in Michigan. This paper reviews the results of these real-world tests and discusses the anomalies and failures that occurred during testing. Citation: Jacob Ward, Patrick Smith, Dan Pierce, David Bevly, Paul Richardson
Ward, Jacob, Smith, Patrick, Pierce, Dan, Bevly, David, Richardson, Paul, Lakshmanan, Sridhar, Argyris, Athanasios, Smyth, Brandon, Adam, Cristian, Heim, Scott
SYSTEMS ARCHITECTURE FOR SEMANTIC INFORMATION-SHARING IN UNMANNED CONVOYS2024-01-37498/7/2018
ABSTRACT Sharing information among vehicles in an unmanned ground vehicle (UGV) convoy allows for improved vehicle performance and reduces the need for each vehicle to be equipped with a full-suite of sensors. Information such as obstacle data, surface properties, and terrain maps are particularly useful for vehicle control and high-level behaviors. This paper describes a system architecture for sharing semantic information among vehicles in a convoy operation. This architecture is demonstrated by sharing terrain information between vehicles in a two-vehicle convoy in both simulation and on actual autonomous vehicles. Update rules fuse information from different sources in a statistical manner and allow for an onboard algorithm to make high-level decisions about the incoming data whether it be from its own sensors or semantic information from other vehicles.
Ferrin, Jeffrey L., Bybee, Taylor C.
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