LINKMOTION “CARBRAIN” INTEGRATING AI & BLOCKCHAIN IN THE CARPUTER Next generation carputer With the growing mobility dynamics and regulation requirements, there is a need for a carputer that manages, secures and ensures the future transactions that each of the car makers desire to deliver to their consumers.. Linkmotion’s carputer aims to be the enabling platform for car makers trying to adopt and revolutionize their forward-thinking goals and stay connected, secured, cloud managed and mobility protected. The development of the carputer to the “CarBrain” will support the integration of AI and Blockchain applications and technology architecture. Next Gen Smart Car Platform Introduction to this document The focus of this paper is to discuss how the software-designed cars integrate and utilize AI and blockchain and why it’s very significant for us at Link Motion. The most robost way to incorporate these attributes and intelligence into cars is within the Carputer and this document serves a crucial role to elaborating this message to our customers throughout the automotive landscape. AI plays a significant role in bringing cross platforms in Automotive ecosystem together. It allows the OEM and end customers with skills and services to be managed and executed. AI performs several key steps and helps to manage the entire business cycle both in manned and autonomous vehicles. Blockchain plays a crucial role in fulfilling the various needs of marketplace transactions with a supremely secured ledger and management across the car life, manufacturing, sales and mobility dynamics. There is a demand for cars to become safer, more comfortable and easier to use, even though complexity is rapidly rising. Technology helps to achieve this goal. Linkmotion’s carputer brings these aspects together in a way that enables the car makers and manufacturers to deliver great products to their customers. The CarBrain will further this goal by supporting the integration of AI and Blockchain applications and technology architecture. Transportation is becoming increasingly intelligent to become more efficient. Better efficiency makes transportation a more critical part of the overall city and economic infrastructure. This makes the technology solutions very important for an efficient and secure operation of overall transportation within cities and economies. Introduction of new technologies will make it easier to manage new-age transportation infrastructure and improve systems over time. Next Gen Smart Car Platform Background on AI Usage of AI has exploded in various IT industries creating both solutions to historic problems as well as creating solutions and problems in the present and future. This dynamic will become more significant in the future. In the past, present and future, the gaming world has experienced tremendous advancements and many players like Nvidia, Intel and next-gen startups are making use of these experiences to develop the next-gen in Automotive needs. These solutions range from simple personal assistant skills all the way to the most sophisticated and fully autonomous capabilities. AI has been successfully used to implement completely new kinds of applications and re-implement existing problems to achieve better / simpler alternatives. Car Production, AI in Mobility Services SCM, Mfg TAAS- Transportation as a Service Fleet Optimizations and Services & Mobility & New Telematics Platforms Car management, Telematics AI in Voice assistants and customer engagements. Car Cognition V2V, V2I and B2V Full Customer Sales & engagement Marketing communications Figure 1 Usage of AI throughout the cycle in automotive AI technology has been under tremendous development and there is constant innovation happening in the area. The automobile industry has also caught up with various AI-based development culiminating in the major application of a fully autonomous and self-driving car. AI development is also creating a myriad of other applications such as driver guidance, vehicle control automatization and increasing driver / passenger comfort with a personal assistant. Next Gen Smart Car Platform Challenge: How to develop a successful vehicle software ecosystem? In-vehicle telematics & telemetry services OBD-II based dongles / smartphones have been winning over integrated systems, even though the latter is technically more sound and the former one often less secure. Why has that happened? • Time-to-market and market access • OEM is vehicle expert, but there might be others who know more about target application The tide is turning however and more and more services are being integrated into the vehicle. Integrated solutions are more robust, secure and easier to use. However, the cost is often higher and the time to enter markets has been significantly longer. For example, the vehicle industry has managed to win over the use case, but how long it can continue before more agile approaches out run it? Vehicle manufacturers have many advantages compared to aftermarket options that make integrated applications technically more attractive: • Access to all data in car, compared to smaller set of data available through OBD-II port • Integration into vehicle embedded system enabling always on operation and power management • Integration into vehicle control systems instead of separate device So far, vehicle manufacturers haven’t been willing to open access to embedded systems. This is due to competition factors, safety aspects and security concerns. However, opening access would enable benefits for the ecosystem for vehicle-based applications: • Embrace software intensive approach, make it possible to write SW for vehicle. Replace applications used in external devices and provide better alternatives for integrated ones. It can be done internally, within limited partner ecosystem or in public. • Move focus from intelligent centralized service to intelligent agents in vehicles allowing more feature-rich scenarios • Open up vehicles for competition in software space What is preventing this? • Security issues leading to possible liabilities -> create protected “container” for execution of SW components, that allows hybrid ecosystems • Not possible to ensure deterministic behavior of vehicle -> Ensure deterministic characteristics of vehicle in case of AI, ”API” management, examples exist such as W3C / Genivi • Fear of platformization in vehicles leading to duopoly seen in mobile devices Figure 2 Airconnect with IBM Watson cognitive services (source: IBM Watson blog) Next Gen Smart Car Platform Part of the strength of AI lies in acknowledging that it may provide unexpected results sometimes and this is accepted by the creators and users of the applications. This is orthogonal to traditional safety critical systems development which starts fully deterministic control of the system to be able to verify the system behavior in every possible situation. There are a couple of options in how to manage the integration going forward: • It is possible to build such deterministic behavior into the AI processor, like Nvidia has done. • An other approach would be to add a deterministic layer in-between that would ensure the safety of the vehicle. • AI could be also be completely separated from such safety critical systems Figure 4 Integration of AI with controller vs with deterministic layer[redraw Building such critical software picture] components is in any case a significantly challenging task that requires deep understanding of software engineering. However, separating the AI with a controlled layer could bring benefits and it might be the only viable approach to realize complex AI-based applications in cyber-physical systems. Applications for consumers appear to be mostly coming from an IT domain or vehicle domain, however, some examples of hybrids do exist. Applications from each direction have specific advantages. Applications are of a completely new kind, which combine real-world objects with cyber domain information management. Next Gen Smart Car Platform Figure 5 How to create complex applications for cyber-physical systems ? IT domain-based services are big in information processing and agility of companies. Introduction of AI & blockchain have widened this gap as it allows more rapid development of applications while being deployed. IT domain companies are pushing towards expanding their domain into the vehicle engineering by pushing software platforms to be incorporated into vehicles. Examples: Android Auto, Apple Carplay, MS Azure Edge. Disadvantages of IT domain based services are centralized characteristics and exposure to complex real- life objects, such as cars. Integration of complex IoT devices is something that is only starting to take off in the industry. Vehicle domain-based services’ strengths lie in the precise control of vehicles with an ability to access all information related to the vehicle. Development of embedded computing platforms has made it possible to implement more and more sophisticated applications within an embedded domain, that can control vehicles with better precision. Vehicle originating services are pushing towards cloud-based approaches by implementing their own services, which is often slow and lacking behind ’IT native’ companies. Technologies used in cloud oriented applications, such as AI and Block chain are being adopted to vehicle-based systems as well. Disadvantages of vehicle domain-based services are long development timeframes and transaction-type business / development models. Obviously the winner of the ultimate race