Simulation of Mixed Critical In-vehicular Networks

Future automotive applications ranging from advanced driver assistance to autonomous driving will largely increase demands on in-vehicular networks. Data flows of high bandwidth or low latency requirements, but in particular many additional communication relations will introduce a new level of complexity to the in-car communication system. It is expected that future communication backbones which interconnect sensors and actuators with ECU in cars will be built on Ethernet technologies. However, signalling from different application domains demands for network services of tailored attributes, including real-time transmission protocols as defined in the TSN Ethernet extensions. These QoS constraints will increase network complexity even further. Event-based simulation is a key technology to master the challenges of an in-car network design. This chapter introduces the domain-specific aspects and simulation models for in-vehicular networks and presents an overview of the car-centric network design process. Starting from a domain specific description language, we cover the corresponding simulation models with their workflows and apply our approach to a related case study for an in-car network of a premium car

Performance improvements of automobile communication protocols in electromagnetic interference environments

Electromagnetic Interference (EMI) is frequently encountered in automobile communication systems due to a large number of inductive nodes used in these systems. This thesis investigates the effects of EMI on two types of automobile communication systems, the Controller Area Network (CAN) and the FlexRay. It also proposes a modified Automatic Repeat reQuest (ARQ) scheme to improve the communication performances in EMI environments --Abstract, page iii

Formal verification of automotive embedded UML designs

Software applications are increasingly dominating safety critical domains. Safety critical domains are domains where the failure of any application could impact human lives. Software application safety has been overlooked for quite some time but more focus and attention is currently directed to this area due to the exponential growth of software embedded applications. Software systems have continuously faced challenges in managing complexity associated with functional growth, flexibility of systems so that they can be easily modified, scalability of solutions across several product lines, quality and reliability of systems, and finally the ability to detect defects early in design phases. AUTOSAR was established to develop open standards to address these challenges. ISO-26262, automotive functional safety standard, aims to ensure functional safety of automotive systems by providing requirements and processes to govern software lifecycle to ensure safety. Each functional system needs to be classified in terms of safety goals, risks and Automotive Safety Integrity Level (ASIL: A, B, C and D) with ASIL D denoting the most stringent safety level. As risk of the system increases, ASIL level increases and the standard mandates more stringent methods to ensure safety. ISO-26262 mandates that ASILs C and D classified systems utilize walkthrough, semi-formal verification, inspection, control flow analysis, data flow analysis, static code analysis and semantic code analysis techniques to verify software unit design and implementation. Ensuring software specification compliance via formal methods has remained an academic endeavor for quite some time. Several factors discourage formal methods adoption in the industry. One major factor is the complexity of using formal methods. Software specification compliance in automotive remains in the bulk heavily dependent on traceability matrix, human based reviews, and testing activities conducted on either actual production software level or simulation level. ISO26262 automotive safety standard recommends, although not strongly, using formal notations in automotive systems that exhibit high risk in case of failure yet the industry still heavily relies on semi-formal notations such as UML. The use of semi-formal notations makes specification compliance still heavily dependent on manual processes and testing efforts. In this research, we propose a framework where UML finite state machines are compiled into formal notations, specification requirements are mapped into formal model theorems and SAT/SMT solvers are utilized to validate implementation compliance to specification. The framework will allow semi-formal verification of AUTOSAR UML designs via an automated formal framework backbone. This semi-formal verification framework will allow automotive software to comply with ISO-26262 ASIL C and D unit design and implementation formal verification guideline. Semi-formal UML finite state machines are automatically compiled into formal notations based on Symbolic Analysis Laboratory formal notation. Requirements are captured in the UML design and compiled automatically into theorems. Model Checkers are run against the compiled formal model and theorems to detect counterexamples that violate the requirements in the UML model. Semi-formal verification of the design allows us to uncover issues that were previously detected in testing and production stages. The methodology is applied on several automotive systems to show how the framework automates the verification of UML based designs, the de-facto standard for automotive systems design, based on an implicit formal methodology while hiding the cons that discouraged the industry from using it. Additionally, the framework automates ISO-26262 system design verification guideline which would otherwise be verified via human error prone approaches

Conformance Testing for the AUTOSAR Standard

International audienceThe paper presents why AUTOSAR conformance tests are required, what has been achieved, and how 3 car manufacturers will use conformance tests as part of their vehicle E/E engineering process. Important topics covered are the need for conformance testing when developing a standard, the relationship between conformance and interoperability, the need for interoperability of ECUs in a vehicle, and the need to avoid diverging implementation of a standard

A novel framework for vehicle functions identification by exploiting machine learning techniques

openNowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification.Nowadays vehicles architectures exploit various automotive network protocols that bring information between the implemented Electronic Central Units (ECUs). Exchanged data are encoded and only Original Equipment Manufacturers (OEMs) and T1 (Tier One) producers know their meaning and how decode them. A software model will be developed in order to detect vehicles functions without having database files associated to network signals. Furthermore, the model will behave like an ECU by producing output signals related to input ones. Machine Learning techniques will be exploited, in particular Clustering task will be exploited to understand not a priori known vehicle functions and a Neural Network will be implemented to emulate an ECU behavior. Signals will be grouped in five different types of vehicle functions and the model will predict the ECU’s output data with high accuracy. Applications concerning the developed project are, in primis, to fix up possible vehicles electronics faults. In addiction, vehicle predictive maintenance could be done. Another application, could be to check by OEMs if T1 manufacturers comply the required specification