The Benefits of a Unified Product Experience
The market for machine learning algorithms for autonomous driving is still relatively young and, as a result, remains highly fragmented. With so many companies and organizations developing their own unique solutions and technologies, one must go to one company for data collection, another for management, and others for model development, MLOps, and even deployment. It is difficult to navigate an industry when there has not yet been any significant market consolidation. This fragmentation has led to a diverse range of approaches and technologies, resulting in a highly competitive and rapidly evolving market.
Different companies and organizations use different hardware, software, and algorithms and there are few existing standards and protocols around verification & validation as standardizations are still being set in the industry. This leads to a lack of interoperability and operational bottlenecks due to data transfers and format conversions.
ML can play a key role in developing standardization and protocols in autonomous driving development, by providing the means to ensure the safety and reliability of the technology and make it more easily adoptable by the public. However this doesn’t necessarily address the segmentation that is also occurring.
Streamlined and mature processes evolve over time and are accelerated by collaboration, consolidation, and innovation. A machine learning platform specific to autonomous driving can enhance the technology by optimizing data management, processing and analysis of data from sensors such as cameras, lidar and radar. In addition to being versatile and able to support a multitude of file formats and software through data abstraction, such a platform could provide a unified product experience which would allow data to flow as needed, creating feedback loops and the ability to use the data for model training. It enables real-time feature extraction, localization, model development, and deployment.
File format standardization: By using common file formats for data storage and sharing, companies and organizations can reduce the need for format conversions, which can save time and resources. This can be achieved by developing open-source file formats and encouraging their use in the industry.
Software standardization: By using common software among companies and organizations, the need for training on different software can be reduced, making it easier for engineers to work on different projects and for teams to collaborate. This can be achieved by developing open-source software and encouraging its use in the industry.
Interoperability: A unified platform can also promote interoperability among autonomous vehicles from different companies and organizations, by providing a common set of APIs and interfaces for communication and control. This can make it easier for vehicles to share information and coordinate with each other on the road, increasing safety and efficiency.
Consistency: A platform can also promote consistency in the user experience and performance of autonomous vehicles, by providing a common set of guidelines and standards for vehicle design, software development and testing. This can help ensure that all vehicles meet a certain level of quality and safety, and make it easier for the public to understand and trust the technology.
RoadMentor is a platform that stands to bring a unified product experience to ML model development for ADAS and autonomous driving. RoadMentor can help to reduce operational bottlenecks, increase interoperability and safety, and promote public trust in autonomous driving technology by standardizing the file formats, software and experience across the industry.