Research suggests that large automotive OEMs can boost their operating profits by up to 16% by deploying AI at scale, provided they focus their AI investments in the right place. To realize this advantage in the autonomous driving arena, companies need to build or acquire competencies in a number of specific areas, for example:
Intelligence: OEMs must build a detailed understanding of the role played by intelligence at all levels of autonomy and make sure that they can obtain, validate, verify, and standardize that intelligence.
Data: As we have seen, higher levels of autonomous driving functionality require computation and decision-making around large numbers of situations and decisions, and these depend on access to intelligence derived from large volumes of crowdsourced data. It is important to be able to manage and process that data effectively. AI and data competencies together are required to enable all the data to be stored, annotated, visualized, analyzed, and shared effectively.
Connectivity: Autonomous driving can be facilitated by better connectivity, which enables many aspects of the vehicle to be updated over the air (OTA). Coupled with the provision of functionality in software rather than hardware wherever possible, this helps to ensure that all vehicles are benefiting from the latest intelligence and technology.
Communication: 5G will be pivotal to enabling autonomous driving on public roads. For autonomous driving to work, enormous volumes of data have to be transferred, whether we are looking at automatic vehicle-to-vehicle (V2V) communications or the vehicle-to-everything (V2X) communication required for the anticipated smart infrastructure for roads. By accelerating connection speed and reducing latency, 5G enables vehicles to communicate almost instantly with each other and a huge number of connected on-road assets and infrastructure.