Roadmap Compression via Streamlining MLOps Infrastructure and its Impacts on ADAS programs
Advanced Driver Assistance Systems (ADAS) are becoming increasingly important in the automotive industry as they offer a wide range of features that enhance the safety and comfort of the driving experience. However, the development and deployment of ADAS systems can be a complex and time-consuming process. Traditional hand-coded algorithms and feature engineering, while they have allowed for the development of basic L0-L2 ADAS systems, are no longer sufficient for developing L2+ ADAS systems, which require more advanced capabilities such as decision-making and prediction.
This is where the integration of machine learning (ML) and machine learning operations (MLOps) can help automotive manufacturers to scale their ADAS development efforts. A machine learning framework, combined with MLOps and integrated annotation tools, can provide significant benefits in terms of cost and time savings, as well as improved system accuracy and performance.
MLOps is a set of practices that aim to optimize the collaboration and integration of machine learning in software development. This includes data and annotation management, model training, and deployment. By integrating ML into annotation tools, companies can automate the process of data labeling, which is a crucial step in training and validating machine learning models. This can help to compress the automotive design and development roadmap by 6-9 months, which can save billions of dollars in development costs and enable companies to bring their products to market much faster.
A more efficient and accurate ADAS system also leads to safer vehicles on the road, which can bring significant savings in terms of insurance and medical costs. For example, features like automatic emergency braking and lane departure warning can significantly reduce the number of accidents caused by human error. Furthermore, advanced features like adaptive cruise control and traffic sign recognition can enhance the driving experience and make it more comfortable and safe.
It is worth noting that implementing a machine learning framework and integrating MLOps and annotation tools in the development process requires a large investment in infrastructure and tools. Additionally, it requires a significant amount of data to be able to train models in the first place. However, the payoff can be significant in the long run as the improved efficiency and accuracy of the ADAS system can lead to significant cost and time savings, as well as improved safety on the road.
Build vs Buy
Machine Learning Operations (MLOps) is a rapidly growing field that involves managing the development and deployment of machine learning models. Some companies choose to handle this process in-house by building their own MLOps team, while others opt to outsource this work to a third-party provider.
One of the main benefits of having an in-house MLOps team is that the company has full control over the development and deployment of their models. This allows for greater flexibility and customization of the process to fit the specific needs of the organization. Additionally, having an in-house team means that the company is not reliant on external vendors and can make decisions on a faster pace.
However, there are also several drawbacks to having an in-house MLOps team. One of the main challenges is that building and maintaining an MLOps infrastructure can be time-consuming and expensive. Additionally, having an in-house MLOps team means that the company will also need to devote resources to the infrastructure and maintenance of the system, which can be a significant distraction from the company’s core competencies.
On the other hand, using a third-party provider for MLOps can have several benefits. One of the main advantages is that the provider is responsible for the infrastructure and maintenance of the system, which frees up the internal team to focus on the development and optimization of the models. Additionally, a third-party provider can bring a wealth of expertise and experience to the table, which can help to ensure that the company is utilizing best practices for MLOps. Furthermore, a third-party provider can help to reduce costs and increase scalability, enabling the company to have access to the latest technology and resources without the need to invest heavily in building and maintaining them internally.
Furthermore, a 3rd party provider also can provide specialized knowledge that the internal team might not possess. For example, an MLOps provider may have experience in deploying models in a specific industry or technology, which can be particularly valuable if the company is trying to break into a new market.
In conclusion, the decision of whether to handle MLOps in-house or to use a third-party provider will depend on the specific needs and resources of the organization. While having an in-house team can provide greater control and flexibility, it can also be expensive and time-consuming. On the other hand, a third-party provider can provide expertise, scalability and can free up the internal team to focus on more immediate results and not worry about uptime and infrastructure maintenance, making it an attractive option for companies looking to improve efficiency and performance in their ML-driven operations.
Using an external, application-specific MLOps team that understands the Advanced Driver Assistance Systems (ADAS) can deliver even greater value to an organization than a general MLOps provider. This is because an ADAS-specific MLOps team will have a deep understanding of the design, development, and production cycles of the automotive industry, which can be critical for the success of ADAS projects.
One of the main advantages of working with an external, application-specific MLOps team is that they can provide curated experience and understanding of the nuances and challenges specific to the ADAS sector, which can be especially useful for Original Equipment Manufacturers (OEMs) and suppliers. For example, such provider can understand how to work with the ADAS-specific data and requirements for deploying the models on the cars, having a knowledge of the best practices in testing and certifying the ADAS models before deployment.
An ADAS-specific MLOps team can also help to streamline operations and increase efficiency by identifying and addressing common issues that arise during the design, development, and production of ADAS systems. They can provide expertise on the best practices, frameworks and technologies that have been proven to work in the industry, this will allow the company to cut down on experimentation and trial-and-error and instead focus on delivering faster results to the consumers.
Additionally, an ADAS-specific MLOps team can help to ensure that the company is meeting all of the regulatory requirements for ADAS systems and can help to navigate the complex web of standards and certifications that are required in the automotive industry.
In conclusion, working with an external, application-specific MLOps team that has deep knowledge and understanding of ADAS can provide a significant advantage to OEMs and suppliers. They bring a curated expertise to the table that can help to streamline operations, improve efficiency, and deliver faster results to consumers. Furthermore, they can provide a valuable understanding of the design, development, and production cycles of the automotive industry, as well as regulatory requirements, which can help to ensure that the ADAS systems are developed and deployed successfully.
ASILD, ASPICE, ISO 26262
When developing Advanced Driver Assistance Systems (ADAS) it is critical to ensure that the systems meet various regulatory and industry standards, such as ASPICE, ASILD and others. These standards provide a framework for ensuring that the systems are designed and developed in a safe and reliable manner, and that they meet certain requirements for functionality, safety and performance.
ASPICE, which stands for Automotive SPICE, is a process assessment model that is widely used in the automotive industry. It provides a standard for evaluating the maturity and capability of an organization’s development process, and includes guidelines for areas such as project planning, requirements management, and testing. Compliance with ASPICE can help to ensure that the development process of ADAS systems is consistent, repeatable, and meets a certain level of quality.
ASILD, which stands for Automotive Safety Integrity Level, is a safety standard that is used to evaluate the safety of ADAS systems. This standard provides a framework for determining the risk level of the system, and includes guidelines for areas such as hazard analysis, risk assessment, and safety testing. Compliance with ASILD can help to ensure that the system is designed and developed in a safe manner, and that it meets certain requirements for safety performance.
In addition to ASPICE and ASILD, there are also other verification and validation requirements for ADAS systems. For example, standards like ISO26262, which is focused on functional safety for road vehicles, and SOTIF (Safety of the Intended Functionality) which defines requirements for the development of ADAS systems and other automated driving systems.
Having an external, application-specific MLOps team that is familiar with these standards can be particularly valuable, as they can provide guidance and expertise on how to meet these requirements during the development of the ADAS systems. They can also provide best practices for testing and certifying the system to meet these standards, and ensure compliance with the safety and quality regulations, leading to a more robust and reliable systems and a smoother path to deployment.
In summary, meeting standards such as ASPICE, ASILD and others, is essential for ensuring that ADAS systems are designed, developed and deployed in a safe and reliable manner. These standards provide a framework for evaluating the maturity, capability, and safety performance of the systems, and compliance with these standards is critical for gaining the trust of customers and regulatory authorities. An application-specific MLOps team that understands these standards and regulations, can be very valuable in helping organizations navigate the compliance process, leading to a more robust and reliable end product.
Advanced Driver Assistance Systems (ADAS) rely on a variety of sub-systems, such as mapping, localization, perception, and planning, to provide the necessary functionality and safety features. In order for these systems to be certified for standards such as ASPICE, ISO 26262, and ASILD, certain requirements need to be met in the development, testing, and validation of these sub-systems.
Mapping and localization systems are responsible for providing the ADAS system with an accurate and up-to-date map of the environment, as well as determining the vehicle’s location within that environment. In order to be certified for standards such as ASPICE, these systems must meet requirements for accuracy, reliability, and repeatability, and must be tested to ensure they meet these standards. This includes, but not limited to, testing the system on different environments, different lighting conditions and weather, and ensuring that the system can handle errors or outliers in the data, and that it can recover from such errors.
Perception systems are responsible for interpreting the data from the vehicle’s sensors, such as cameras and lidar, and identifying relevant information, such as other vehicles, pedestrians, and obstacles. In order to be certified for standards such as ASILD, these systems must be designed to ensure that the failure of any single component does not lead to a dangerous situation, and that the system can detect and react to hazards in the environment. This includes, but not limited to, testing the system on different scenarios such as different angles, different speeds and different weather conditions, and ensuring that the system can detect and react to different hazards with a high level of reliability.
Planning systems are responsible for creating a plan of action based on the information provided by the mapping, localization, and perception systems. In order to be certified for standards such as ISO 26262, these systems must meet requirements for functional safety, including hazard analysis, risk assessment, and safe state analysis. This includes, but not limited to, ensuring that the system can plan safe trajectories that avoid obstacles and that the system can handle errors or outliers in the data, and that it can recover from such errors.
In conclusion, certifying ADAS systems for standards such as ASPICE, ISO 26262, and ASILD requires a rigorous development, testing and validation process that involves ensuring that all sub-systems such as mapping, localization, perception and planning meet certain safety, quality and reliability standards.
Having an external, application-specific MLOps team that understands these standards and regulations and possess knowledge on how to test and certify these subsystems for the standards, can be very valuable in helping organizations navigate the compliance process. These teams can provide guidance and expertise on how to meet these requirements during the development, testing and certification of the ADAS systems and ensure compliance with the safety and quality regulations, leading to a more robust and reliable end product.
In conclusion, the integration of machine learning and machine learning operations in the development of ADAS systems can significantly enhance the speed, accuracy and performance of the systems while enabling cost-effective solution. Automotive manufacturers that can take advantage of these technologies will be well-positioned to compete in the market and offer safer and more advanced vehicles to their customers.