The launch of a self-driving vehicle has become a decisive focal point of automotive stakeholders. They have invested billions of dollars and put their best resources for completing the task of making autonomous cars a reality. Currently, a vehicle is equipped with a variety of electronic components and advanced driver assistance systems (ADAS) to sense the environment around it and exchange signals with entities outside it. The entire process of communication with external entities requires a huge amount of data that is generated and used to implement machine learning with algorithms in the computer system of the vehicle. This is how OEMs and automotive Tier I suppliers along with non-automotive players plan to make autonomous vehicles a reality. Hence, reliable automotive software is the ultimate key to implementing AI and develop semi-autonomous and autonomous cars.
Leading automobile manufacturers plan to launch autonomous vehicles by 2020. For instance, General Motors has declared to commercialize its autonomous vehicles in metro cities in 2019, whereas, the tentative year for Ford is 2021. Apart from OEMs and other players of the automotive ecosystem, startups are unleashing distinctive types of software platforms for autonomous cars. However, there is still a long way to go for the actual launch of autonomous vehicles because there is a need to develop precise lines of code and AI to process them in a computer small enough to fit in a car. There are developments done for automotive software, but there are limitations of automotive software in the autonomous space that need to be fulfilled. Therefore, automotive software offers a lot of revenue generating opportunities in semi-autonomous and autonomous car technologies.
MarketsandMarkets™ View Point:
Srinath Manda – Associate Director : Automotive & Transportation at MarketsandMarkets™, shares his point of view as mentioned below :
As the number of connected technologies continues to increase year on year, various companies from non-automotive industry are trying to increase their revenues by providing services based on the data generated by connected and autonomous cars. Automotive manufacturers estimate that the data generated by connected vehicles will be measured in gigabytes and terabytes per hour by 2020. The increase in data generation can be attributed to the integration of hardware and software platforms into various automotive applications to get real-time status of vehicles (diagnostics and maintenance) and their drivers (driving patterns and history). The data will further be used by various stakeholders (OEMs, banks, regulators, and consumers) of the ecosystem that provide end-to-end connected vehicle solutions to the consumers. Hence, data would be the most valuable source of revenue for companies. There is a need to monetize the data and provide customized services to each vehicle and its driver. Thus, the increase in data-driven services provides a major growth opportunity for the automotive software market.
Driverless cars are still in a nascent stage and are currently being tested on the road in Texas, Arizona, California, Pennsylvania, Washington, and Michigan under specific test areas and driving conditions. These cars would soon be a part of mainstream reality according to automotive experts. Jen-Hsun Huang, CEO of NVIDIA, announced at Bosch Connected World 2017 in Berlin that NVIDIA will offer technology enabling Level-4 autonomous driving capabilities by the end of 2018. Similarly, BMW is also expected to unveil its self-driving electric vehicle, BMW iNext, by 2021. These developments would offer several opportunities to cloud and cybersecurity service providers to come forward and partner with automotive companies to take the driving revolution ahead with robust vehicle security.
e- Estimated; p- Projected | Source: MarketsandMarkets™ Analysis
Importance of software for self-driving vehicles
Today, the global automotive industry is on the cusp of technological transformation and holds the promise that can put in motion a remarkable, advanced safety inside the vehicle as well as on roads. The introduction of automated vehicle safety technologies may prove to be the greatest development in personal transportation. An automated system inside a vehicle is a combination of hardware and software that performs driving functions, with or without a human driver. The National Highway Traffic Safety Administration (NHTSA) represents SAE levels 3, 4, and 5 in highly automated vehicles (HAVs). The advent of HAV capabilities requires functionality convergence, superior computing power, and a high degree of integration. HAVs run with highly complex systems such as ADAS, LIDAR, radar, video camera module, ultrasonic sensors, GPS, and central computer. These complex systems gather and analyze huge amount of data every day or every hour. RTOS, Linux OS, ECUs with high storage, and advanced communication systems with high bandwidth for communication are required to run these complicated systems effectively. Software must be programmed with millions of lines of codes. Moreover, software and firmware must be regularly updated with OTA facilities to avoid any vulnerabilities.
The technology and products required for a self-driving vehicle are being tested and getting advanced day by day. However, the bigger question about the self-driving car would arise when the supporting technology and infrastructure from consortiums and government authorities are ready. While several pilot projects have been undertaken around the globe, they have been carried out in a controlled environment. In a real-time environment, the use of a self-driving car has resulted in some mishaps in recent years. In March 2018, an experimental Uber self-driving car struck and killed a pedestrian. Therefore, the development of supporting infrastructure for HAVs is a key challenge for the entire ecosystem (OEM, Tier I, Tier II, governments, and regulatory authorities). A number of car manufacturers have partnered with Tier I and Tier II suppliers to ensure the success of their pilot projects. Companies have also followed the strategy of acquiring autonomous vehicle or automotive software providers. For instance, in October 2017, Aptiv acquired autonomous software developer nuTonomy Inc. The objective of the acquisition was to bring more than 60 Aptiv-branded autonomous cars on the road by the end of 2017. Similarly, in January 2017, Volvo announced a joint venture with Autoliv for self-driving cars. In June 2018, Volvo announced a partnership with LIDAR startup Luminar—a company that works on both physical and car-mounted sensors and the software designed to process, label, and tag captured data.
Competitive scenario among automotive software providers
Development and testing in the field of self-driving vehicles have encouraged automotive software providers to innovate and launch various innovative solutions & products as well as adopt strategies of mergers & acquisitions and partnership with OEMs, tier-1, and tier-2 suppliers. For instance, in June 2017, BlackBerry QNX launched QNX Hypervisor 2.0 that addresses safety and security—two of the most important factors for next-generation connected and autonomous vehicle software. This real-time Type 1 Hypervisor based on QNX SDP 7.0, BlackBerry’s most advanced and secure 64-bit embedded operating system, enables developers to partition and isolate safety-critical environments from non-safety-critical environments, ensuring that no critical systems are put at risk. In December 2015, BlackBerry QNX and AdasWorks announced a technological collaboration focused on ADAS and autonomous vehicles. As part of the new initiative, the companies would port several AdasWorks functions, including lane detection, moving-object detection, and object classification, to QNX OS platforms. In April 2015, Wind River introduced Automotive Profile for VxWorks, an AUTOSAR-compliant software, to help customers develop ISO 26262 certifiable automotive safety-critical applications such as ADAS to piloted and autonomous driving.
Increasing research and development in the field of self-driving technology has accentuated the need for instruction codes (software line of codes) to perform electronic operations, which in turn creates better opportunities for automotive software providers to focus on innovating software developments and setting up standards of software development for the entire ecosystem and value chain.