Browse Topic: Cooperative driving automation

Items (42)

Simulation and Experimental Evaluation of a Vehicle–Road Cooperative Positioning Method in Global Navigation Satellite Systems (GNSS)-Denied Environments

12-09-04-0029

7/29/2026

In complex urban environments, vehicle positioning based on Global Navigation Satellite Systems (GNSS) is prone to failure or accuracy degradation due to signal blockage and multipath effects. To address this issue, this article proposes a vehicle–road cooperative positioning method based on factor graph optimization for GNSS-denied environments and evaluates its performance through both simulation and real-vehicle experiments. In the proposed approach, road codes deployed on the road surface serve as absolute position references on the road surface, and high-precision vehicle position estimates are obtained in real time by fusing roadside positioning information with onboard sensor measurements using factor graph optimization. Furthermore, to reduce the experimental cost and development cycle of the vehicle–road cooperative positioning method, a performance simulation platform is developed based on the CARLA simulator, RoadRunner, and the CARLA-ROS (Robot Operating System) bridge for real-time communication between simulation and positioning modules. The platform supports road code generation and deployment, customized scenario construction, and positioning performance simulation, and a multi-objective optimization approach is employed to obtain an optimal deployment scheme for road code spacing. Finally, the effectiveness of the proposed vehicle–road cooperative positioning method is validated through both simulation and real-vehicle experiments. Real-vehicle experiments show that the proposed method reduces the average RMSE (Root Mean Square Error) by 26.6% compared with the ESKF (Error State Kalman Filter) baseline, achieving an RMSE of 0.25 m and a maximum error of 0.95 m at a vehicle speed of 60 km/h and a 10 m code spacing, thereby confirming decimeter-level continuous accuracy in GNSS-denied environments.

Shen, Chuanfu, Zhao, Zhiguo, Yan, Danshu, Ling, Yubin

Impact of Network-Induced Delays on Autonomous Vehicle Steering Stability

10-10-03-0024

6/25/2026

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

Automated Design Sustainability Evaluation Framework: A CAD-Integrated Tool for Real-Time Costing and LCA in Automotive Engineering

2026-37-0044

6/9/2026

As the automotive industry faces increasingly rigorous environmental regulations and an approaching obligation for Digital Product Passports (DPPs), incorporating sustainability metrics into the early design phase has become a necessity. Traditionally, Life Cycle Assessment (LCA) and manufacturing cost estimation are performed during or after the design phase using specific methods and tools, resulting in costly iterations and delayed decision-making. This paper introduces a preliminary computational tool that combines 3D CAD and spreadsheet software via VBA integration. The framework automates the generation of an “Extended Bill of Materials” by extracting geometric and manufacturing data directly from CAD models. This tool’s classification logic is a key innovation that intelligently processes CAD features to identify component categories, such as sheet metal, machined parts, or plastic injections. This automated recognition allows the framework to implement specific algorithmic models for the preliminary estimation of production costs and environmental impact indicators. The gap between computer-aided design and sustainability analysis is partially bridged by the tool, enabling engineers to receive immediate feedback on the carbon footprint and recyclability of their designs during the early conceptual stage. Preliminary testing within automotive case studies shows a substantial decrease in lead times for technical estimation. Specifically, analysis time was reduced by at least 90%, with subsystems processed in under 10 minutes, a significant improvement over traditional manual calculations. This tool represents a pragmatic step toward “Circular Design” paradigms, supporting compliance with future legislative frameworks and fostering the transition toward a circular economy in transportation systems.

Guadagno, Maurizio, Cecconi, Leonardo, Berzi, Lorenzo, Delogu, Massimo

A Simple Analytical Model for Predicting Cooperative Ratio in Agreement-Seeking Cooperative Driving Automation with C-V2X Communication

2026-01-0078

4/7/2026

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 Experiments

2026-01-0077

4/7/2026

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 Approach

2026-01-0463

4/7/2026

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

Dynamic Switching Control of Vehicle Platoon with Kalman Filter Compensator Considering Communication Delays and Packet Dropouts

2025-01-7341

12/31/2025

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

Commercial Vehicles: Vol. 2

EPRCOMPV05202512/29/2025

Hsu, Tsung-Ming, Muelaner, Jody Emlyn

Enhanced Cooperative Perception through Asynchronous Vehicle-to-Infrastructure Framework with Delay Mitigation for Connected and Automated Vehicles

12-09-01-0008

8/8/2025

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 contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this article proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on-board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the CAV’s perception range, giving it enough time and space to react to the adversary vehicles.

Saravanan, Nithish Kumar, Jammula, Varun Chandra, Yang, Yezhou, Wishart, Jeffrey, Zhao, Junfeng

DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking

2025-01-0294

7/2/2025

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 dangerous situations. Based on this measure, alerts are generated and transmitted to road users equipped with an accident prevention app. An important component of DISRUPT is the development of a digital twin for testing and optimizing the overall system and its individual components. The main feature of the digital twin is the simulation of a photorealistic virtual copy of the test field environment. This enables the simulation of radar, infrared and conventional visible light cameras, which are combined with simulated data transmission delays to replicate the real system as accurately as possible. The plug-and-play design of the digital twin, together with a toolset for running and analyzing numerous simulations, enables efficient and thorough testing of the tracking and prediction algorithms. In particular, the digital twin enables the generation of hazard scenarios that are very unlikely to be observed in everyday traffic.

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 Vehicles

J3216_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

Enhanced Adaptive Cruise Control Strategies Considering Front and Rear Vehicles

10-09-01-0007

1/29/2025

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

A Reinforcement Learning-Based Parameter Tuning Approach for a Secure Cooperative Adaptive Cruise Control System

12-08-04-0033

1/25/2025

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 controller, observer, and FDI attack estimator, ensuring the safety and security of the developed CACC under varying conditions. We evaluated the performance of the developed controller before and after optimizing parameters, and the results show about a 50% improvement in accuracy of the FDI attack estimation and a 76% enhancement in safe following distance with the optimized controller in each scenario.

Javidi-Niroumand, Farahnaz, Sargolzaei, Arman

Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems

2024-01-7028

12/13/2024

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 system. In order to fuse the multi-agent and multi-modal features, we use a bird’s-eye view (BEV) space as the consistent and unified feature representations and employ a transformer-based network for effective multi-agent multi-modal BEV feature fusion. We validate our method on the OPV2V and V2XSim benchmarks, achieving state-of-the-art performance in 3D cooperative perception tasks. The proposed framework significantly improves object detection accuracy and robustness, especially in complex traffic scenarios with occlusions such as dense intersections.

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 Space

2024-01-7025

11/15/2024

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 is performed; The vehicle trajectory is smoothed based on a Bezier curve. Finally, the simulation results show the trajectory planning results in unstructured space and two-dimensional plane. Compared to the sand cat swarm optimization (SCSO) algorithm, the fitness function value of the trajectory planned by the SCSHPSO algorithm in unstructured environment has decreased by 6.34%. The simulation results demonstrate the performance of the CPS based trajectory planning scheme for connected-automated vehicles designed in this paper, especially in unstructured environments, where the SCSHPSO algorithm is more competitive.

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 Influence

12-08-03-0025

10/22/2024

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 the AD system to decide if sensor information from the V2X field should be directly incorporated or handled with care.

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 Vehicles

12-07-04-0030

9/6/2024

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 example objectives. RAD-VEGA then cooperatively solves this MOOP, taking into account the availability of AV’s onboard resources and the application layer characteristics of today’s ITS communication tools. The performance of the proposed mechanism is evaluated by solving the ZDT1 test problem and studying pareto-frontier solutions to the true front over time. The scalability of the proposed solution is estimated to be 305 CAVs, considering a communication bandwidth of 6 MB/s. Additionally, cooperative AV planning scenario examples are simulated, and the effectiveness of the proposed mechanism is demonstrated by comparing final and initial solutions after solving the MOOP using RAD-VEGA.

Ghahremaninejad, Reza, Bilgen, Semih

A Methodology to Enhance Transparency for Trustworthy Artificial Intelligence for Cooperative, Connected, and Automated Mobility

12-08-01-0010

9/2/2024

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 Guidance

J3282_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 Uncertainties

2024-37-0010

6/12/2024

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 Economy

2024-01-2564

4/9/2024

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

A Guidance Strategy for Parking Space of Autonomous Valet Parking with Cooperative Vehicle Infrastructure System

2024-01-2571

4/9/2024

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 falling into the local optimal solution in early time. The first improvement preserves the optimal solution for each iteration, and the second improvement changes pheromone evaporation coefficient according to certain rules. The illustrative example shows that the method presented in this paper can reduce the phenomenon of repeated berth seeking and the cost of parking system.

Zhang, Zhouping, Liu, Weidong, Sun, Zhipeng, Zhu, Zuwei, Hu, Yiming, Zeng, Dequan

Cooperative Driving Automation (CDA) Feature - Infrastructure-Based Prescriptive Cooperative Merge

J3256_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 Season

12-07-02-0012

11/14/2023

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 time gap rose from 3 to 5 sec, and the average engagement distance of all platooning trips was 1.92 km. In the review of the platooning effect on the powertrain system, it was observed that fluctuations in the follower truck’s engine power were generally larger compared to those of the lead truck. Furthermore, when trucks performed platooning on the flat road segments, the follower truck saved fuel; however, on the road segments with grade changes, the freight transportation specific fuel consumption (kg/(ton·100 km)) of the follower truck increased. Moreover, the freight transportation specific fuel consumption of the follower truck was 25.8% more than that of the lead truck when cut-ins and cut-outs occurred. Test results show that the frequency of cut-ins increased from 1.6 to 5 times per hour when time gap increased from 3 to 5 sec. Overall, beyond successful and safe commercial truck platooning operations during the cold winter season, no substantial benefit of fuel saving was observed in the investigated platform.

Jiang, Luo, Kheyrollahi, Javad, Koch, Charles Robert, Shahbakhti, Mahdi

Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ ASN File

J2945/6A_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. Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ SET File that includes the J2945/6 ASN File and the J2945/6 PDF (ZIP format) - download purchase only.

V2X Core Technical Committee

Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ SET File

J2945/6S_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. Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning™ J2945/6 ASN File that includes the ASN file (ZIP format) - download purchase only.

V2X Core Technical Committee

Performance Requirements for Cooperative Adaptive Cruise Control (CACC) and Platooning

J2945/6_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 Vehicles

10-07-04-0031

9/4/2023

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 perturbations are modeled as a constrained robust multiagent Markov decision process. Meanwhile, a nonlinear constraint setting with Kullback–Leibler divergence is developed to keep the variation of the driving policy perturbed by stochastic perturbations within bounds. Additionally, a robust multiagent policy optimization approach is proposed to approximate the optimal robust coordinated driving policy. Finally, we evaluate the proposed robust coordinated decision-making method in three highway scenarios with different traffic densities. Quantitatively, in the absence of noises, the proposed method achieves an approximate average enhancement of 25.58% in traffic efficiency and 91.31% in safety compared to all baselines across the three scenarios. In the presence of noises, our technique improves traffic efficiency and safety by an approximate average of 30.81% and 81.02% compared to all baselines in the three scenarios, respectively. The results demonstrate that the proposed approach is capable of improving automated driving performance and ensuring policy robustness against observational uncertainties.

He, Xiangkun, Chen, Hao, Lv, Chen

Cooperative Driving Automation (CDA) Feature: Perception Status Sharing for Occluded Pedestrian Collision Avoidance

J3251_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 VEHICLES

2024-01-4059

8/10/2023

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 fails to complete the mission when degraded, however the cooperative fleet succeeds, while also enabling the degraded AGV to regain operational status. Citation: M. Ashley, D. McMullan, S. Gopalswamy, “Physically Cooperating Autonomous Ground Vehicles,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.

Ashley, Michiel, McMullan, Davis, Gopalswamy, Swaminathan

Process for Developing an Interoperable Cooperative Driving Use Case Test Framework and Test Procedures

J3252_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 Environment

2023-01-0689

4/11/2023

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-life deployable technology, we also consider passenger comfort in our MPC design, which is a relevant aspect that is often a conflicting objective with respect to energy efficiency. Our simulation scenario is characterized by a homogeneous string of three battery electric vehicles and was modelled in a MATLAB/Simulink environment. An extensive set of simulation experiments forms the basis for our discussion on the energy-saving potential of cooperative driving automation systems.

Musa, Alessia, Miretti, Federico, Misul, Daniela

Application Protocol and Requirements for Maneuver Sharing and Coordinating Service

J3186_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 Traffic

12-05-04-0025

4/21/2022

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 merging in mixed traffic as a study case, and establish human-in-the-loop (HuiL) simulations. The results show that our proposed platform can facilitate data collection and performance assessment for modeling personalized behaviors and interactions in CADS under various traffic scenarios.

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 Traffic

SAE-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 merging in mixed traffic as a study case, and establish Human-in-the-Loop (HuiL) simulations. The results show that our proposed platform can facilitate data collection and performance assessment for modeling personalized behaviors and interactions in CADS under various traffic scenarios.

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 Vehicles

J3216_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. 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 application. The needs may be expressed in terms of performance characteristics, such as latency, transmission mode (e.g., one-way, two-way), range, privacy and security, and information content and quality. There are several potential technologies for communicating information between the subject vehicle and other participants. This document focuses on application-oriented functionality and does not imply the need for or require any specific functionality associated with communications protocols or the open systems interconnection model layers in a protocol stack. This document addresses the operational and tactical timescales of dynamic driving on ADS-operated vehicles, and excludes strategic functions such as trip scheduling and selection of destinations and waypoints. This information report is intended to facilitate communication and awareness for the design and anticipated development and validation of cooperative driving automation.

Cooperative Driving Automation(CDA) Committee

Using Demanded Power and RDE Aggressiveness Metrics to Analyze the Impact of CACC Aggressiveness on Heavy Duty Platooning Power Consumption

2021-01-0069

4/6/2021

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 potential energy savings. Therefore, the impact of the controller on the observed driving style was analyzed in detail. In order to quantify the transient operational characteristics of the experimental trials, metrics from the European Real Driving Emissions (RDE) legislation were modified to serve as metrics of aggressiveness during platooning. The metrics (v ⋅ apos)95 and Relative Positive Acceleration (RPA) were calculated for platooning and non-platooning runs. These results indicate that the CACC induces small acceleration events during platooning to retain the commanded longitudinal separation between vehicles. These small acceleration events increase following vehicle aggressiveness during platooning and prevent the following vehicles from obtaining maximum energy savings. Moreover, a correlation between the RDE metric (v ⋅ apos)95 and energy savings is developed. Hence, this work establishes the ability of RDE metrics to assess CACC impacts on platoon energy savings.

Siefert, Jan, Stegner, Evan, Snitzer, Philip, Ward, Jacob, Bevly, David M., Hoffman, Mark, Kotz, Andrew

SEMANTIC DIGITAL SURFACE MAP TOWARDS COLLABORATIVE OFF-ROAD VEHICLE AUTONOMY

2024-01-3877

8/11/2020

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 CNN models. Citation: H. J. J. Brand, B. Li, “Semantic Digital Surface Map Towards Collaborative Off-Road Vehicle Autonomy”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2020.

Brand, Howard J. J., Li, Bing

Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles

J3216_202005 (Historical)

5/7/2020

Cooperative Driving Automation(CDA) Committee

Analysis of On-Road Highway Testing for a Two Truck Cooperative Adaptive Cruise Control (CACC) Platoon

2020-01-5009

3/27/2020

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 a total platooned distance of approximately 1090 km. Gap distances varied over a range of 18.3-91.4 m (60-300 ft) with speeds of 89-105 km/h (55-65 mph). Fuel economy analysis was calculated from the SAE J1939 CAN bus data. Overall, the results show the feasibility and realizable benefits of CACC systems. Future validation of this CACC platooning system through SAE type II fuel tests is also discussed.

Smith, Patrick, Bevly, David

Cooperative Adaptive Cruise Control (CACC) in Controlled and Real-World Environments: Testing and Results

2024-01-3825

8/13/2019

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, Sridhar Lakshmanan, Athanasios Argyris, Brandon Smyth, Cristian Adam, Scott Heim “Cooperative Adaptive Cruise Control (CACC) in Controlled and Real-World Environments: Testing and Results”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.

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 CONVOYS

2024-01-3749

8/7/2018

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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