The future is now: the digital transformation enables new possibilities for Automotive, which not long ago sounded more like a very distant future or even science fiction. Examples are solutions for Autonomous Driving (esp. Highly Automated Driving) and increased usage of Artificial Intelligence such as Deep Learning to extend algorithm capabilities further.
The Automotive Industry has recognized this potential, and we are currently witnessing more and more investments into the abovementioned fields: new organization units and teams get formed, there are investments in startups and, last but not least, strategic cooperations and partnerships are formed between different players. This holds true both for Development as well as Manufacturing.
One major topic for the Development of Automotive Products is the transformation towards Autonomous Driving. The main topics in progress today and in future, to only name a few, are: 1) use of Artificial Intelligence (AI) to provide new algorithms or extend/improve existing ones, 2) high precision in positioning and localization in navigation (GPS, GNSS etc. combined with HD maps) with the goal to navigate without human input, 3) Data Fusion by combining different technologies such as Lidar, Radar and Camera to increase the confidence in environment perception, 4) Transition to Fail Operational systems which maintain a limited functionality even in case of failure.
Similarly, also Automotive Manufacturing undergoes transformations and changes. This is especially well summarized using terms such as Industry 4.0, Industrial Internet of Things, Smart Factory etc. The main idea here is to have everything connected and communicating with each other. Each process and machine deliver data using sensors that are analyzed in the Cloud using Sensor Fusion and Industrial Data Analysis and hence enable several use cases. This supports the improvement of manufacturing efficiency and flexibility, increased operational knowledge, prediction of machine failures and not least lower manufacturing costs. Also, in this field, AI is increasingly in use e.g. for Industrial Data Analytics and Preventive Maintenance.
The current transformations and challenges in Automotive Product Development and Automotive Manufacturing are driving the increased need for efficient Product Safety. In its “Product Integrity,” Volume the German Association of the Automotive Industry (VDA) defines Product Safety as follows: “A product that is available on the market may not jeopardize the health and safety of any person during its intended [use] or foreseeable misuse.” HELLA follows this definition and understands Product Safety as a holistic approach combining the following aspects: Functional Safety, Electrical Safety, Chemical Safety, Mechanical Safety, Safety of Intended Functionality and Cyber Security. Product Safety affects the whole Product Life Cycle; it ranges from the early phase of Conceptual Design, Development, Testing, and Manufacturing to Operation, Service, and Decommissioning.
A set of measures for covering different aspects of Product Safety is spread across multiple norms and standards: The basic standard for Functional Safety of Electrical/ Electronic/Programmable Electronic safety-related Systems is the IEC61508. Different industries and application fields have adapted IEC61508 to their specific characteristics. For instance, ISO26262 is an adaption for the Automotive Industry, IEC61511 for the Process Industry, IEC61131-6 for Programmable Controllers, IEC62061 for Machinery etc. Another essential standard for the Safety of Machinery is the ISO13849. The IEC 61784-3 provides requirements on the Safety of Industrial Communication Networks whereas the IEC62443 covers its security requirements. The upcoming ISO/ SAE 21434 aims at covering requirements on Cyber Security for the engineering of Road Vehicles.
The right selection and combination of the available measures are challenging as they in part, cover different aspects and are not fully synchronized with each other. In addition, the handling of comparably novel topics such as the Safety of AI is not yet fully addressed by the existing norms. To cope with the increasing complexity of Automotive Product Development and Automotive Manufacturing, an Approach for Product Safety is required, which shall not substitute the existing approaches but bring them together, combine and extend them to a holistic system-based approach. It fulfills the following criteria:
• Product Safety starts early on in the Conceptual Design phase. Here the holistic multidisciplinary Systems Engineering approach is essential.
• It is crucial to anticipate the Product Life Cycle in the early phases of the Product Creation to minimize the potential costly late iteration loops. This is also the prerequisite for the evidence of an attractive balance between RoI and Safety throughout the whole market phase.
• Product and Manufacturing System shall be developed in an integrative way. Already starting with the Conceptual Design and Product Development on System Level, the requirements resulting from safety on the Manufacturing systems are to be defined (e.g., Definition and Provision of Special Characteristics, Requirements on the Manufacturing Tools, Requirements on ICT, EOL, Preventive Maintenance etc.).
• The Safety of the Manufacturing System itself is also assured.
This is not about reaching some targets or metrics only, nor solely about meeting a process requirement. It is more about establishing Safety Thinking and the right Approach to Product Safety from early on. The focus is on the systematic identification and examination of the problem and the evidence that the chosen implemented measures are effective in minimizing the risks to an acceptable level. The successful implementation certainly requires an upfront investment; in a broader mid-term perspective, it pays off.