The technological advancements of recent years have steadily increased the complexity of vehicle-internal software systems, and the ongoing development towards autonomous driving will further aggravate this situation. This is leading to a level of complexity that is pushing the limits of existing vehicle software architectures and system designs. By changing the software structure to a service-based architecture, companies in other domains successfully managed the rising complexity and created a more agile and future-oriented development process. This paper presents a case-study investigating the feasibility and possible effects of changing the software architecture for a complex driver assistance function to a microservice architecture. The complete procedure is described, starting with the description of the software-environment and the corresponding requirements, followed by the implementation, and the final testing. In addition, this paper provides a high-level evaluation of the microservice architecture for the automotive use-case. The results show that microservice architectures can reduce complexity and time-consuming process steps and makes the automotive software systems prepared for upcoming challenges as long as the principles of microservice architectures are carefully followed.
An alternative development environment is described by Kugele @cite_2 . In their work, the throughput of the OpenDDS software middleware is evaluated. Furthermore, a formal mapping of services of the data distribution service (DDS) and a case-study about fail-operational behavior are described. The paper concludes that the used SOA technique in combination with DCPS is suitable for the automotive industry, but some points regarding safety, certification, and security could not be clarified. Kugele point out that a commercial version of DDS could solve these problems. Also, DDS in combination with the Docker platform is a suitable development method in an agile work environment.
@cite_2
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Abstract Visual tracking algorithms based on structured output support vector machine (SOSVM) have demonstrated excellent performance. However, sampling methods and optimization strategies of SOSVM undesirably increase the computational overloads, which hinder real-time application of these algorithms. Moreover, due to the lack of high-dimensional features and dense training samples, SOSVM-based algorithms are unstable to deal with various challenging scenarios, such as occlusions and scale variations. Recently, visual tracking algorithms based on discriminative correlation filters (DCF), especially the combination of DCF and features from deep convolutional neural networks (CNN), have been successfully applied to visual tracking, and attains surprisingly good performance on recent benchmarks. The success is mainly attributed to two aspects: the circular correlation properties of DCF and the powerful representation capabilities of CNN features. Nevertheless, compared with SOSVM, DCF-based algorithms are restricted to simple ridge regression which has a weaker discriminative ability. In this paper, a novel circular and structural operator tracker (CSOT) is proposed for high performance visual tracking, it not only possesses the powerful discriminative capability of SOSVM but also efficiently inherits the superior computational efficiency of DCF. Based on the proposed circular and structural operators, a set of primal confidence score maps can be obtained by circular correlating feature maps with their corresponding structural correlation filters. Furthermore, an implicit interpolation is applied to convert the multi-resolution feature maps to the continuous domain and make all primal confidence score maps have the same spatial resolution. Then, we exploit an efficient ensemble post-processor based on relative entropy, which can coalesce primal confidence score maps and create an optimal confidence score map for more accurate localization. The target is localized on the peak of the optimal confidence score map. Besides, we introduce a collaborative optimization strategy to update circular and structural operators by iteratively training structural correlation filters, which significantly reduces computational complexity and improves robustness. Experimental results demonstrate that our approach achieves state-of-the-art performance in mean AUC scores of 71.5 and 69.4 on the OTB2013 and OTB2015 benchmarks respectively, and obtains a third-best expected average overlap (EAO) score of 29.8 on the VOT2017 benchmark. Middleware for robotics development must meet demanding requirements in real-time distributed embedded systems. The Robot Operating System (ROS), open-source middleware, has been widely used for robotics applications. However, the ROS is not suitable for real-time embedded systems because it does not satisfy real-time requirements and only runs on a few OSs. To address this problem, ROS1 will undergo a significant upgrade to ROS2 by utilizing the Data Distribution Service (DDS). DDS is suitable for real-time distributed embedded systems due to its various transport configurations (e.g., deadline and fault-tolerance) and scalability. ROS2 must convert data for DDS and abstract DDS from its users; however, this incurs additional overhead, which is examined in this study. Transport latencies between ROS2 nodes vary depending on the use cases, data size, configurations, and DDS vendors. We conduct proof of concept for DDS approach to ROS and arrange DDS characteristic and guidelines from various evaluations. By highlighting the DDS capabilities, we explore and evaluate the potential and constraints of DDS and ROS2. We present a solution for enabling standard compliant channel access for a fully software-based Software Defined Radio (SDR) architecture. With the availability of a GNU Radio implementation of an Orthogonal Frequency Division Multiplexing (OFDM) transceiver, there is substantial demand for standard compliant channel access. It has been shown that implementation of CSMA on a host PC is infeasible due to system-inherent delays. The common approach is to fully implement the protocol stack on the FPGA, which makes further updates or modifications to the protocols a complex and time consuming task. We take another approach and investigate the feasibility of a fully software-based solution and show that standard compliant broadcast transmissions are possible with marginal modifications of the FPGA. We envision the use of our system for example in the vehicular networking domain, where broadcast is the main communication paradigm. We show that our SDR solution exactly complies with the IEEE 802.11 Distributed Coordination Function (DCF) as well as Enhanced Distributed Channel Access (EDCA) timings. We were even able to identify shortcomings of commercial systems and prototypes. In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs), which deal with processing of unbounded streams of continuous data at scale distributedly in real or near-real time. A fundamental problem in a DSDPS is the scheduling problem (i.e., assigning workload to workers machines) with the objective of minimizing average end-to-end tuple processing time. A widely-used solution is to distribute workload evenly over machines in the cluster in a round-robin manner, which is obviously not efficient due to lack of consideration for communication delay. Model-based approaches (such as queueing theory) do not work well either due to the high complexity of the system environment. We aim to develop a novel model-free approach that can learn to well control a DSDPS from its experience rather than accurate and mathematically solvable system models, just as a human learns a skill (such as cooking, driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks (DNNs). To validate and evaluate the proposed framework, we implemented it based on a widely-used DSDPS, Apache Storm, and tested it with three representative applications: continuous queries, log stream processing and word count (stream version). Extensive experimental results show 1) Compared to Storm's default scheduler and the state-of-the-art model-based method, the proposed framework reduces average tuple processing by 33.5 and 14.0 respectively on average. 2) The proposed framework can quickly reach a good scheduling solution during online learning, which justifies its practicability for online control in DSDPSs.