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Intellectual property rights and their protection is a cornerstone of the automotive value chain. The automotive industry is composed by a meshwork of tightly integrated organizations that cooperate and compete in a hierarchical marketplace. Trading know-how and other virtual assets between participants is an essential part of this business. Thereby, software as a medium to transport ideas, innovations, and technologies plays a particular role. Protection of virtual goods and their associated rights is a current issue whose solution will determine how business will be done in the future automotive market. Automotive experts and researchers agree that ICT security technologies are a vital part to implement such a market. In this paper we examine the software life cycle of an automotive Electronic Control Unit (ECU) and discuss potential threats and countermeasures for each stage.

Permanently increasing software complexity of today's electronic control units (ECUs) makes testing a central and significant task within embedded software development. While new software functions are still being developed or optimized, other functions already undergo certain tests, mostly on module level but also on system and integration level. Testing must be done as early as possible within the automotive development process. Typically ECU software developers test new function modules by stimulating the code with test data and capturing the modules' output behavior to compare it with reference data. This paper presents a new and systematic way of testing embedded software for automotive electronics, called MTest. MTest combines the classical module test with model-based development. The central element of MTest is the classification-tree method, which has originally been developed by the DaimlerChrysler research department.

In the automotive industry, the model-based approach is increasingly establishing itself as a standard paradigm for developing control unit software. Just as code reviews are widespread in classical software development as a quality assurance measure, models also have to undergo a stringent review procedure – particularly if they serve as a starting point for automatic implementation by code generators. In addition to these model reviews, the generated production code is reviewed later in the development process by performing auto code reviews. This article will present procedures for and give an account of experiences with model and code reviews which have been adapted to the model-based development process.

Whereas the verification of non-safety-related, embedded software typically focuses on demonstrating that the implementation fulfills its functional requirements, this is not sufficient for safety-relevant systems. In this case, the control software must also meet application-specific safety requirements. Safety requirements typically arise from the application of hazard and/or safety analysis techniques, e.g. FMEA, FTA or SHARD. During the downstream development process it must be shown that these requirements cannot be violated. This can be achieved utilizing different techniques. One way of providing evidence that violations of the safety properties identified cannot occur is to thoroughly test each of the safety requirements. This paper introduces Evolutionary Safety Testing (EST), a fully automated procedure for the safety testing of embedded control software.

Because of the rapidly increasing amount of electronic components and busses in a vehicle, the use of gateways in Electronic Control Units (ECUs) becomes more important. The upcoming question is how to design an optimal gateway. This paper describes a method for designing an optimal automotive gateway in an FPGA by using Evolutionary Algorithms (EAs). The complete gateway functionality is diagrammed in a specification graph which consists of a function graph and an architecture graph. The function graph describes the complete functionality of the gateway. The architecture graph shows the variety of the different implementation options of the mapped function graph. Each gateway task in the function graph can be realized either in a parallel way (different kinds of hardware implementations) or in a sequential way (software on a microprocessor core).

Increasing vehicle complexity, broader vehicle variety, and global vehicle projects are a major challenge to all global vehicle manufacturers. Common vehicle projects not only require a common E/E-architecture in order to share components, but also a common diagnostic strategy. On-board and off-board strategies have been developed to achieve this goal. This paper will explain the importance of ISO-standardization, common networking & diagnostic architectures and standardized access to vehicle electronics. Also the need for a common diagnostic process chain throughout engineering, manufacturing and service, and the need for harmonized vehicle standards between passenger cars and heavy duty trucks will be addressed.

The increase of active safety will demand more and more electronic intelligence, if a drastic optimization of conventional systems is not possible any more. Starting from today's mechatronic systems, the trend leads via tomorrow's smart electronic systems to the future electronic networking of all intelligent vehicle systems. The paper describes the present status of these systems in Europe and the possibilities of increasing the active safety by using electronic intelligence.