Local intelligence’s role in fully automated mobility
The path towards automated, autonomous mobility is complex to the extent that there is no single path to achieve it.
However, this transition it is either viewed as an automaker initiative or as a technology disruptor. Location intelligence plays its part in this process. It entails the collation, analysis and distribution of geospatial data to provide a digital representation of the environment to increase situational awareness of automated and autonomous vehicles.
Jørgen Behrens, senior vice-president and chief product officer at HERE Technologies adds that it involves both an evolutionary and revolutionary path. He explains: “The evolutionary path means adding new automated driving capabilities to passenger and commercial vehicles, to increase the safety and convenience by taking over routine driving tasks from the driver. However, there is still the need of a driver supervising and taking over, which is why it is most often referred as conditional automation. The advantage of this path is that can be deployed at scale, and it is relatively affordable to own.”
“The revolutionary path aims to replace the driver all together and it pursued as an alternative to human chauffeurs or drivers in ride hailing services and geofenced logistics. There are multiple societal benefits for highly or fully autonomous mobility, but due to the costs and complexity of operating without human supervision it will probably take some time until this will become a mobility solution available at scale.”
Mark Cracknell, head of connected and automated mobility at Zenzic, reveals that his company is working on The UK Connected and Automated Mobility Roadmap to 2030, which establishes the path and vision for the connected and automated future of mobility in the UK. In terms of evolutionary paths, or even revolutionary paths, he says the report contains four overarching themes.
He said: “The first being society and people; ensuring public acceptance, legislation and insurance and licensing and use, including vehicle approvals. The second one is vehicles: their software, machine learning, hardware, and sensors. The third is infrastructure, and underneath that is communications, the emerging road infrastructure, and the digital infrastructure. The fourth and final path is around services.”
In essence the paths are about how the vehicles are used, and type of mode of transport employed as part of an integrated pathway to fully automated mobility. For example, this could involve passenger transport with, for example, autonomous taxi or bus services, or freight and logistics such as an automated delivery van.
A key backbone to the integration of these different modes of transport is communication, and the ability to provide local intelligence to each vehicle and throughout the system. Speaking about why there is a need for local intelligence, Cracknell adds: “There is an opportunity with connectivity and autonomous to provide transport equity for the disabled and elderly, or for people who feel the cost of transport is high. They can provide a lower cost transport service in a broader area.”
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