As the automotive industry continues its push toward software-defined vehicles, development teams must understand how artificial intelligence (AI) and machine learning (ML) affect them.
The growing use of AI and ML technologies in automotive software, both on the manufacturing floor and onboard vehicles, requires rethinking the approach functional safety processes and certification, says Mark Pitchford, technical specialist at LDRA.
Manufacturers and suppliers have already begun the work, and functional safety standards are following close behind. AI/ML is being used in automotive and the steps being taken to maintain safety-critical software practices within the industry.
Generative AI has fueled much confusion and uncertainty around AI/ML, even as manufacturers like Mercedes-Benz are using the technology. Adopting a broader scope of these technologies helps to encompass all potential possibilities for safety-critical automotive software.
In general, AI can be classified into two categories. Automotive software teams have been using narrow AI for decades. Consider an airbag ECU or a digital instrument cluster, each of which is built to perform specific tasks and often falls under traditional hazard analysis and risk assessment processes.
More recently, self-driving systems employ AI/ML to ingest sensor data and adapt to their environments for controlling functions like steering and braking. AI/ML applications also extend into software development environments.
Automated unit test vector generation is a form of narrow AI as it simulates intelligent behavior by deriving test stubs and control from existing code.
Audi worked with Intel and Nebbiolo on an AI-based proof-of-concept (POC) to improve quality control processes for the welds on its vehicles. The POC took place at Audi’s factory in Neckarsulm, Germany, which has 2,500 autonomous robots on its production line.
Before the POC, Audi used the industry’s standard manual sampling method of pulling one car off the line daily to test welding spots and record their quality. The objective of the POC was to inspect all 5,000 welds for every car and infer the results of each weld within microseconds.
Toward this end, developers created and trained an ML algorithm for accuracy by comparing its predictions with real inspection data. The software used data on electric voltage and current curves during the welding operation, configurations of the welds, types of metal, and the health of the electrodes.
The automotive industry’s approach to incorporating AI/ML into standards is to define best practices for AI/ML applications that enable developers to fulfill the objectives of long-standing, higher-level standards such as ISO 26262.
There are few, if any, safety-critical systems based entirely on AI/ML. More common is the integration of AI/ML-based elements within conventionally engineered systems, where the concepts of domain separation and “freedom from interference” can be applied.
ISO 26262 defines freedom from interference as the “absence of cascading failures between two or more elements that could lead to the violation of a safety requirement.”
As illustrated below, traditional taint analysis tools can test and validate data flows coming from AI/ML elements. Developers can check incoming and outgoing data, based on data ranges and control flows, to determine the potential safety risks between connected elements.
From optimizing manufacturing processes to controlling driver functions, AI/ML-based systems are the new frontier for automotive manufacturers. Despite the grand promise of AI/ML acting with human intelligence, automotive software teams must still apply functional safety processes and tools to minimize risks.
For the foreseeable future, AI/ML components will operate inside conventionally developed software and rely on having humans in the loop. New safety standards are being developed to guide teams on the future of these technologies, and developers must know how to combine them with traditional verification techniques to achieve their goals.