Design Criteria to Architect Continuous Experimentation for Self-Driving Vehicles

The software powering today's vehicles surpasses mechatronics as the dominating engineering challenge due to its fast evolving and innovative nature. In addition, the software and system architecture for upcoming vehicles with automated driving functionality is already processing ~750MB/s - corresponding to over 180 simultaneous 4K-video streams from popular video-on-demand services. Hence, self-driving cars will run so much software to resemble "small data centers on wheels" rather than just transportation vehicles. Continuous Integration, Deployment, and Experimentation have been successfully adopted for software-only products as enabling methodology for feedback-based software development. For example, a popular search engine conducts ~250 experiments each day to improve the software based on its users' behavior. This work investigates design criteria for the software architecture and the corresponding software development and deployment process for complex cyber-physical systems, with the goal of enabling Continuous Experimentation as a way to achieve continuous software evolution. Our research involved reviewing related literature on the topic to extract relevant design requirements. The study is concluded by describing the software development and deployment process and software architecture adopted by our self-driving vehicle laboratory, both based on the extracted criteria.Comment: Copyright 2017 IEEE. Paper submitted and accepted at the 2017 IEEE International Conference on Software Architecture. 8 pages, 2 figures. Published in IEEE Xplore Digital Library, URL: http://ieeexplore.ieee.org/abstract/document/7930218

Bridging the Experimental Gap: Applying Continuous Experimentation to the Field of Cyber-Physical Systems, in the Example of the Automotive Domain

In the software world frequent updates and fast delivery of new features are needed by companies to bring value to customers and not lag behind competition. When in cyber-physical systems the software functionality dominates in importance the hardware capabilities, the same speed in creating new value is needed by the product owners to differentiate their products and attract customers. The automotive field is an example of a domain that will face this challenge as the industry races to achieve self-driving vehicles, which will necessarily be software-intensive highly complex cyber-physical systems. A software engineering practice capable of accelerating and guiding the software production process using real-world data is Continuous Experimentation. This practice proved to be valuable in software-intensive web-based systems, allowing data-driven software evolution. It involves the use of experiments, which are instrumented versions of the software to be tested, deployed to the actual systems and executed in a limited way alongside the official software version. Valuable data on the future behavior of the prospective feature is collected in this way as it was fed the same real-world data it would encounter once approved and deployed. Additionally, in those cases where an experimental software version can be run as a replacement for the official version, relevant data regarding the system-user interaction can be gathered. In this thesis, the field of cyber-physical systems and the automotive practitioners\u27 perspective on Continuous Experimentation are sampled employing a literature review and a series of case studies. A set of necessary architectural characteristics are defined and possible methods to overcome the issue of resource constraints in cyber-physical systems are proposed in two exploratory studies. Finally, a design study shows and analyses a prototype of a Continuous Experimentation cycle that was designed and executed in a project partnered by Revere, the Chalmers University of Technology\u27s laboratory for vehicle research

Introducing Continuous Experimentation on Resource-Constrained Cyber-Physical Systems

Software is ubiquitous and shapes our world, but at the same time it can be viewed as a plastic resource offering the possibility to be improved even after its deployment to better serve its purpose. Exploiting this possibility, the Continuous Experimentation practice is gaining momentum on connected software-intensive web-based systems, allowing the product owners to deploy "experiments" on their software systems, i.e., experimental instrumented versions of the software monitoring its performances with respect to a predefined set of target metrics, and to use this data to drive their products\u27 evolution.Unfortunately the software that runs on physical units is not as easily re-deployed: cyber-physical systems, i.e., systems that interact with the physical world to perform their operations, may be in hard-to-reach places or moving in the environment, making the process difficult or energetically disadvantageous. Furthermore, such systems are often designed to have just enough hardware resources to perform their duties, having little computational resources left to perform additional tasks, such as performance monitoring.This thesis explores the possibility to enable the\ua0Continuous Experimentation practice for distributed software running on resource-constrained\ua0cyber-physical systems on the example of self-driving vehicles, with the long-term goal of providing a way to continuously improve the quality of these systems\u27 performances. To achieve this, the included studies analyzed, proposed, and designed their contributions in order to provide suitable first steps for the adoption of this practice to the field which is still an open research question. Firstly, an analysis of the advantages and disadvantages that\ua0Continuous Experimentation could bring to the field was carried out. Then, key architectural characteristics capable to enable\ua0Continuous Experimentation on\ua0cyber-physical systems were identified. Successively, a more in-depth study was conducted to analyze how the\ua0Continuous Experimentation process could cope with the lack of adequate computational resources.Lastly, acknowledging the criticality of the software modules\u27 intercommunication protocol, an analysis of the communication patterns highlighted how bandwidth-efficient alternatives can be developed using contextual knowledge.The main results of this thesis are the key architectural features that allow the adoption of the\ua0Continuous Experimentation practice on resource-constrained cyber-physical systems

Over 60,000\ua0km in a year: remotely collecting large-volume high-quality data from a logistics truck

After the first successful large-scale demonstration of eleven self-driving vehicles at the DARPA Urban Challenge in 2007, research results from the competing teams found their way into advanced driver systems (ADAS) that support typical driving tasks like adaptive cruise control and semi-automated parking. However, as of today, SAE Level 4 vehicles are not commercially available yet, which would allow the driver to be inattentive for longer periods. Hence, SAE Level 3, which represents partial automation yet continuously monitored by a human operator, may provide a step towards a viable SAE Level 4 product especially for commercial freight logistics. However, large amounts of data from such freight operations is needed to study the unique challenges in such use cases. In this paper, we present the system and software architecture of an end-to-end data logging solution, which is capable of recording large volumes of high-quality data. The system is installed in a commercial truck that is in daily operation by a logistics company and hence, the recorded data is only accessible remotely (i.e., over-the-air). We report about the fail-safe system design, initial findings from over one year of operation, as well as our lessons learned. During its first year of operation, the truck was used for 210\ua0days by the logistics company, out of which 193\ua0days were logged resulting in more than 4.5\ua0TB of data from five cameras, two GNSS–IMU sensors, and six on-board vehicle controller area networks (CAN) busses. We demonstrate the value of the proposed end-to-end approach for traffic and driver behavior research by analyzing the uploaded data in the cloud to spot critical events such as unexpected harsh braking maneuvers caused by lane merging operations

Continuous Experimentation and the cyber-physical systems challenge: An overview of the literature and the industrial perspective.

Context: New software development patterns are emerging aiming at accelerating the process of delivering value. One is Continuous Experimentation, which allows to systematically deploy and run instrumented software variants during development phase in order to collect data from the field of application. While currently this practice is used on a daily basis on web-based systems, technical difficulties challenge its adoption in fields where computational resources are constrained, e.g., cyber-physical systems and the automotive industry. Objective: This paper aims at providing an overview of the engagement on the Continuous Experimentation practice in the context of cyber-physical systems.Method: A systematic literature review has been conducted to investigate the link between the practice and the field of application. Additionally, an industrial multiple case study is reported. Results: The study presents the current state-of-the-art regarding Continuous Experimentation in the field of cyber-physical systems. The current perspective of Continuous Experimentation in industry is also reported. Conclusions: The field has not reached maturity yet. More conceptual analyses are found than solution proposals and the state-of-practice is yet to be achieved. However it is expected that in time an increasing number of solutions will be proposed and validated

Influences of Wolbachia (Rickettsiales Rickettsiaceae) on the cellular response to cold stress in Drosophila melanogaster (Diptera Drosophilidae)

Wolbachia pipiensis (Hertig et Wolbach, 1924) is known to manipulate the expression of genes implicated in the metabolism, immunity and reproduction in Drosophila melanogaster (Meigen, 1830). Under stress, cells activate the cellular stress response (CSR). The CSR is a conserved network of pathways regulating identification, check and response to stress, preserving the cellular homeostasis. The CSR involves the unfolded protein response, autophagy, the heat shock response and other subcellular pathways. How Wolbachia affects the CSR has not yet been investigated. Here, we report the influence of Wolbachia infection and cold stress on the expression of the Heat-shock-protein-70Aa (Hsp70Aa), Autophagy-related gene-1 (Atg1) and X box binding protein-1 (Xbp1) genes and the influence of cold stress on the Wolbachia surface protein gene (wsp). The Hsp70Aa, Atg1, and Xbp1 genes were affected by Wolbachia infection since they were found to be up-regulated in the Wolbachia-free flies. After cold stress, the Wolbachia-infected flies showed high expression of the Atg1 and Hsp70Aa genes in comparison to the Wolbachia-free flies. Moreover, cold stress negatively influenced the expression of the wsp gene

L'inscription CIL XIII, 2657

A short presentation of the inscription's notice in CIL, XIII with pictures of it and of the original archive now preserved in Berlin. Le projet porte sur un dossier de 1200 fragments inscrits de marbre du mont Pentélique (près d'Athènes en Grèce), dont quelques uns seulement ont été publiés dans le Corpus Inscriptionum Latinarum à la fin du XIXe siècle. Voici la notice du CIL concernant l'inscription, telle qu'elle apparaît dans le volume XIII consacré aux Trois Gaules et aux Germanies dirig..

Continuous experimentation on cyber-physical systems: Challenges and opportunities

Establishing and mastering continuous experimentation as an instrument in the portfolio of software product managers is of growing importance resulting in continuous renewal of products for continuous user satisfaction. Product managers for purely software-based products like web-based applications found in online web-shops or smartphone apps can monitor usage profiles of their products in the context of their customers\u27 usage (i.e. The "field"). However, in the area of interconnected embedded systems, cyber-physical systems, or with Internet-of-Things (IoT), such continuous experimentation is under-explored and in many cases understandably not considered due to safety considerations. In this position paper, we are outlining challenges and opportunities of continuous experimentation for cyber-physical systems

Systematic Evaluation of Three Data Marshalling Approaches for Distributed Software Systems

Abstract Cyber-physical systems like robots and self-driving vehicles comprise complex software systems. Their software is typically realized as distributed agents that are responsible for dedicated tasks like sensor data handling, sensor data fusion, or action planning. The modular design allows a flexible deployment as well as algorithm encapsulation to exchange software modules where needed. Such distributed software exchanges data using a data marshalling layer to serialize and deserialize data structures between a sending and receiving entity. In this article, we are systematically evaluating Google Protobuf, LCM, and our self-adaptive delta marshalling approach by using a generic description language, of which instances can be composed at runtime. Our results show that Google Protobuf performs well for small messages composed mainly by integral field types; the self-adaptive data marshalling approach is efficient if four or more fields of type double are present, and LCM outperforms both when a mix of many integral and double fields is used