AuNa: Modularly Integrated Simulation Framework for Cooperative Autonomous Navigation

In the near future, the development of autonomous driving will get more complex as the vehicles will not only rely on their own sensors but also communicate with other vehicles and the infrastructure to cooperate and improve the driving experience. Towards this, several research areas, such as robotics, communication, and control, are required to collaborate in order to implement future-ready methods. However, each area focuses on the development of its own components first, while the effects the components may have on the whole system are only considered at a later stage. In this work, we integrate the simulation tools of robotics, communication and control namely ROS2, OMNeT++, and MATLAB to evaluate cooperative driving scenarios. The framework can be utilized to develop the individual components using the designated tools, while the final evaluation can be conducted in a complete scenario, enabling the simulation of advanced multi-robot applications for cooperative driving. Furthermore, it can be used to integrate additional tools, as the integration is done in a modular way. We showcase the framework by demonstrating a platooning scenario under cooperative adaptive cruise control (CACC) and the ETSI ITS-G5 communication architecture. Additionally, we compare the differences of the controller performance between the theoretical analysis and practical case study.Comment: 8 pages, preprint, https://github.com/tu-dortmund-ls12-rt/AuN

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Co-simulated digital twin on the network edge: A vehicle platoon

This paper presents an approach to create high-fidelity models suited for digital twin application of distributed multi-agent cyber–physical systems (CPSs) exploiting the combination of simulation units through co-simulation. This approach allows for managing the complexity of cyber–physical systems by decomposing them into multiple intertwined components tailored to specific domains. The native modular design simplifies the building, testing, prototyping, and extending CPSs compared to monolithic simulator approaches. A system of platoon of vehicles is used as a case study to show the advantages achieved with the proposed approach. Multiple components model the physical dynamics, the communication network and protocol, as well as different control software and external environmental situations. The model of the platooning system is used to compare the performance of Vehicle-to-Vehicle communication against a centralized multi-access edge computing paradigm. Moreover, exploiting the detailed model of vehicle dynamics, different road surface conditions are considered to evaluate the performance of the platooning system. Finally, taking advantage of the co-simulation approach, a solution to drive a platoon in critical road conditions has been proposed. The paper shows how co-simulation and design space exploration can be used for parameter calibration and the design of countermeasures to unsafe situations

The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving have encountered significant challenges because of limited capabilities in perception and interaction with complex traffic environments. Cooperative Driving Automation~(CDA) has been considered a pivotal solution to next-generation automated driving and intelligent transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world traffic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem~(OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete development toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation

A Modular Plasmid Assembly Kit for Multigene Expression, Gene Silencing and Silencing Rescue in Plants

The Golden Gate (GG) modular assembly approach offers a standardized, inexpensive and reliable way to ligate multiple DNA fragments in a pre-defined order in a single-tube reaction. We developed a GG based toolkit for the flexible construction of binary plasmids for transgene expression in plants. Starting from a common set of modules, such as promoters, protein tags and transcribed regions of interest, synthetic genes are assembled, which can be further combined to multigene constructs. As an example, we created T-DNA constructs encoding multiple fluorescent proteins targeted to distinct cellular compartments (nucleus, cytosol, plastids) and demonstrated simultaneous expression of all genes in Nicotiana benthamiana, Lotus japonicus and Arabidopsis thaliana. We assembled an RNA interference (RNAi) module for the construction of intron-spliced hairpin RNA constructs and demonstrated silencing of GFP in N. benthamiana. By combination of the silencing construct together with a codon adapted rescue construct into one vector, our system facilitates genetic complementation and thus confirmation of the causative gene responsible for a given RNAi phenotype. As proof of principle, we silenced a destabilized GFP gene (dGFP) and restored GFP fluorescence by expression of a recoded version of dGFP, which was not targeted by the silencing construct