The role of automotive networking has evolved from supporting wiring harness cost and weight reduction, to fundamentally enabling implementation of most new vehicle functions. Today's in-car distributed systems have 80-plus electronic control units (ECUs) connected by multiple types of networks. Many of the inefficient practices introduced during the early days of “multiplexing” — as networks are often referred to — still prevail. Today's challenges call for a new approach.

According to domain experts, 90 percent of innovation in cars for the foreseeable future will be enabled through the electronic vehicle architecture. Networks constitute a major structural element of the car's EE architecture, and shall be treated as such. Successful implementation of complex distributed functions depends on reliable networks. Configuration of the network — being a shared resource — does represent a sizeable challenge, which has to be solved at the system level.

For well over a decade, design of multiplexed networks in automobiles has been a bottom-up style engineering exercise, with very few exceptions. The approach, focusing on agreed “messages” between the ECUs directly involved, has left the effects of unintentional interactions on the bus to be resolved by testing. “Build-and-test” has been the prevailing paradigm. Faults found in late phases of the car project were expensive, and risky to correct, associated with high risk for introducing secondary errors.

Network design practices of the day often mean early binding and late verification of the design. This is contrary to what the industry is striving for in any other technical area, namely; early verification and late binding of solutions.

Predictability in the time domain is a key factor in achieving reliability and efficiency. Timing requirements describe the behaviors that must occur for a function to be correct. Timing behavior defines what the system will do — in other words, it represents a model of the behavior of the system. Timing requirements are essential, representing the requirements for the design, as well as the goal for testing. As shown in Figure 1, a timing model can be used to describe the different elements — end-to-end, for the entire timing chain.

Timing analysis could be applied to the timing behavior, to produce results, which can be checked against the timing requirements. Known timing requirements and timing behavior are both essential prerequisites for scheduling analysis.

As an example, we should take a closer look at a production proven methodology and the tool — the Vehicle Network Architect (VNA).

A tool implementing the correct timing model can in the simplest case perform analysis of a manually created communication matrix to calculate worst-case message latencies for messages sent over a bus. The results can be checked against deadline requirements, derived from the vehicle function's end-to-end timing requirements. Interestingly, in a more advanced scenario, such a tool can perform complete cluster synthesis and gateway configuration, based on requirements declared in its input files. This mode offers fully automatic frame creation, signal packing and schedule table generation.

To be useful, such a tool has to handle all protocols used in a vehicle, to ensure transparent routing of signals, with guaranteed end-to-end latencies through gateways and multiple bus segments.

The dominating network solutions in automotive EE systems of today are control area network (CAN) and local interconnect network (LIN). To meet the demand for higher bandwidth in safety-critical applications, the industry initiated development of FlexRay. These protocols are expected to co-exist in cars for the foreseeable future. The reason for this is that each protocol has a specific field of application, where it represents the cost optimal solution.

System level design (SLD) is often referred to as the next frontier in electronic design automation (EDA), expected to bring substantial productivity and efficiency gains to the automotive industry. The resulting solutions are correct and optimized by design, rather than being a candidate for lengthy integration testing of components provided by a multitiered and multivendor supply chain.

Such a process cannot be implemented without advanced tools. A fragmented market, characterized by proprietary solutions, specific to each major OEM was not a feasible base for deployment of such products in the past. To realize the potential of SLD, and to turn the attention of the EDA industry toward automotive applications, a paradigm shift was required (Figure 2).