Leveraging Automotive SPICE for Machine Learning in Driver Monitoring Systems

Editor: Petr Švimberský

As vehicles evolve into intelligent systems, there is no question that integrating advanced technologies like machine learning (ML) into automotive applications is an opportunity that must be seized, despite the formidable challenges it presents. A practical example is the development of driver monitoring systems that detect fatigue or distraction. Adherence to Automotive SPICE (ASPICE) standards is essential for structured and reliable development, particularly when incorporating cutting-edge AI models.

Machine learning is an essential component of advanced driver assistance systems (ADAS), particularly for detecting driver fatigue or distraction through facial recognition.

Traditional facial recognition software requires engineers to define specific algorithms for recognizing signs of fatigue, such as eye closure, yawning, or head tilting. This method has its own limitations.

ML models analyze data from in-cabin cameras to identify patterns indicating whether a driver is tired, distracted or alert. This is completely different from traditional software development, where features are predefined by a human engineer.

Machine learning or AI development is incorporated into ASPICE by providing a set of ML engineering processes incorporated into the wider software system development process. This shall be done in parallel to the SWE.3 Software Detailed design and Unit construction and SWE.4 Software Unit verification, to produce component based on ML approach.

ML data management (SUP.11): To succeed with a data-driven machine learning approach, you must prepare a representative and bias-free dataset with appropriate labelling. In our example, It must be considered all possible body types, facial hair styles, wearables, and garments used in different climates, as well as address the challenges posed by diverse physical characteristics to make the dataset representative.

A data science approach guarantees that data is correctly split into training, testing, and validation datasets, free of bias and truly representative.

ML Requirement Analysis (MLE.1): The process must clearly define what the model needs to detect, such as head movements or eye closures. It must also define the level of precision and target certainty, for example, 90% certainty and no more than 20% false positive warnings. These requirements must be precise and aligned with the system’s overall goals.

ML Architecture (MLE.2):The ML component architecture must encompass the ML model type (e.g. convolutional neural network classifier) and any supporting components, such as image preprocessing tools or algorithms that integrate ML outputs into the vehicle’s safety systems.

ML training (MLE.3): The model is trained on extensive datasets that simulate various real-world driving conditions. Hyperparameters (depth or width of network) must be tuned as part of the model validation. It is essential to guarantee data diversity and quality to prevent bias and maintain accuracy across different environments, lighting conditions, and driver behaviors. The SUP.11 ML Data Management Process is the solution here.

ML Model Testing (MLE.4): Once trained, the model must be tested to confirm that it meets the required safety and performance requirements. The model must be tested in both the machine learning environment and the target environment. This is a direct equivalent to the back-to-back testing process used in model-based development. This verification is essential to guarantee that the model operates correctly in real-world contexts and consistently identifies when a driver is losing focus or becoming fatigued.

These structured processes guarantee that machine learning can be integrated into automotive systems without compromising safety, reliability, or compliance with regulatory requirements.

ASPICE ML processes guarantee reliability at both technical and project levels. A thorough analysis of the resources required for training the model and the effort needed to collect and process data is also essential.

ASPICE-driven ML development offers significant benefits. It provides a common language and framework that is essential for effective collaboration between ML engineers and automotive developers, ensuring that solutions meet industry standards.

Date	Booking	Language	Place	Regular price	Book today	Additional Fee Type	Add Fee	Discount
23-Jun-2025 - 24-Jun-2025	calculate price and order	EN	Online, EARLY TIME	1175 €	980 €	Exam	450	Online training 10%, early bird discount 10%, teams >2 10%, teams >4 10%, from best cost country 10%, private/freelancer 10% [max 50%]