Browse Topic: Cooperative driving automation

Items (18)

SYSTEMS ARCHITECTURE FOR SEMANTIC INFORMATION-SHARING IN UNMANNED CONVOYS

2024-01-3749

11/15/2024

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.

PHYSICALLY COOPERATING AUTONOMOUS GROUND VEHICLES

2024-01-4059

11/15/2024

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

SEMANTIC DIGITAL SURFACE MAP TOWARDS COLLABORATIVE OFF-ROAD VEHICLE AUTONOMY

2024-01-3877

11/15/2024

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

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

2024-01-3825

11/15/2024

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

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

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

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

09/06/2024

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 Mobility

12-08-01-0010

09/02/2024

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)

06/26/2024

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

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

J3256_202403 (Current)

03/04/2024

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

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

Robust Multiagent Reinforcement Learning toward Coordinated Decision-Making of Automated Vehicles

10-07-04-0031

09/04/2023

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 Avoidance

J3251_202308 (Current)

08/22/2023

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

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

J3252_202308 (Current)

08/01/2023

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

04/11/2023

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

Co-Simulation Platform for Modeling and Evaluating Connected and Automated Vehicles and Human Behavior in Mixed Traffic

12-05-04-0025

04/21/2022

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

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

J3216_202107 (Current)

07/16/2021

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

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

J3216_202005 (Historical)

05/07/2020

Cooperative Driving Automation(CDA) Committee

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