Abstract We present , a framework for the integration of modern verification tools in the context of AUTOSAR, a widely-used open standard for the development of automotive software systems. Our framework enables the automatic end-to-end verification of system-level properties using a compositional approach. It combines software model checking techniques for the verification of software components at the code level with a contract-based analysis for verifying their correct composition. In this paper, we present the tool through its application on a representative automotive case study, discussing the main functionalities provided and the results obtained.

Analysis models, technologies and tools are extensively used in the automotive domain to validate and optimize the design and implementation of SW systems. This is especially true for modern systems including advanced autonomous (and complex) features. The range of analysis methods that can be applied is extremely wide and goes from functional correctness to functional safety to timing (and schedulability), security, and possibly even more. The AUTOSAR automotive standard has been defined with the purpose of standardizing the SW architecture of automotive systems and enable the construction of systems by composing SW components that are portable and abstract with respect to the underlying HW/SW platform. However, AUTOSAR was originally developed with portability of code in mind, and even if it quickly evolved to include a system-level modeling language (with its metamodel) and later extensions to deal with the needs of analysis methods (and tools), it is hardly comprehensive and still affected by several omissions and limitations. To fix the limitations with respect to timing and schedulability analysis Bosch developed the Amalthea (later App4MC) metamodel and tools. In Huawei, a more general (and ambitious) approach was undertaken to support not only timing analysis, but also model checking (or other types of formal verification), safety analysis and even design optimization. The approach is based on the concepts of a unified (modular) metamodel and a framework based on Eclipse to integrate analysis methods and tools. In this paper we describe the framework and the results obtained with respect to the objectives of functional verification and timing analysis.

This paper introduces the contents and the possible usage of the DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA project. The reference scenario is a domestic environment equipped with a large number of microphones and microphone arrays distributed in space. The corpus is composed of both real and simulated material, and it includes 12 US and 12 UK English native speakers. Each speaker uttered different sets of phonetically-rich sentences, newspaper articles, conversational speech, keywords, and commands. From this material, a large set of 1-minute sequences was generated, which also includes typical domestic background noise as well as inter/intra-room reverberation effects. Dev and test sets were derived, which represent a very precious material for different studies on multi-microphone speech processing and distant-speech recognition. Various tasks and corresponding Kaldi recipes have already been developed. The paper reports a first set of baseline results obtained using different techniques, including Deep Neural Networks (DNN), aligned with the state-of-the-art at international level.

This paper refers to an activity under way at the speech recognition technology level for the development of a hands-free dialogue interaction system in the car environment. The use of a set of HMM recognizers, running in parallel, is being investigated in order to ensure low complexity, modularity, fast response, and to allow a real-time reconfiguration of the language models and grammars according to the policy indicated by natural language understanding and dialogue manager modules. A corpus of spontaneous speech interactions was collected using the Wizard-of-Oz method in a real driving situation with a microphone placed far from the driver. The use of parallel recognition units, each specialized on a given geographical domain, was explored using the resulting real corpus. Experiments show the advantage of selecting the recognized sentence according to the maximum likelihood among the active units when compared to the use of a single language model based on a very large vocabulary.