This is a guest blog by Ben Calnan, head of the smart cities practice at people movement consultancy Movement Strategies
Autonomous vehicles will soon be a reality – in fact, industry commentators believe that the first fully autonomous cars will be available to the UK public in five years’ time and perhaps sooner in other countries. However, for car manufacturers, the transition from prototype development to mainstream deployment is full of challenges. While navigating the issues surrounding data ownership will prove difficult, for those who successfully establish a role in the AV data eco-system, commercial opportunity awaits.
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For driverless cars to work effectively in the real world, they’ll have to integrate with existing infrastructure and transport modes. Modelling the potential demand for AV ownership vs. rental, the effect on public transport usage, the requirement and location of parking and charging facilities, and capacity for cars to ‘circulate’ are all essential. However, while the datasets required to unlock this insight are available, in practice, accessing the relevant information to inform this modelling will not be easy.
Integration and sharing of data is crucial. One of the challenges for smart cities in recent years has been the interface between information and services provided by the public and private sectors. Guaranteeing the quality of data from different parties, as well as navigating the issues surrounding data ownership is a challenge. For example, mapping AV demand would require accessing data from in-vehicle, public transport usage and cellular data tracking. No one organisation can access this information without purchasing from or collaborating with others.
There are now a series of projects and organisations seeking to address these issues such as the Fiware consortium for interoperability standards, and major technology companies are building online data brokerages and promoting API integration. These projects are the real enablers of effective collaborations, as competing automotive industry stakeholders bring their products to market, and cities attempt to facilitate the introduction of this new transport mode.
As well as supporting the predictive analyses needed to accelerate the mainstreaming of AVs in our cities, data collected by AVs themselves will also prove a valuable commodity. Driverless cars need and therefore collect, analyse and combine vast quantities of data as they navigate the road network and the hazards that entails, constantly sending back information to servers, helping to improve their algorithms. In this sense, they are the ultimate environmental data collectors, frequently updating a virtual picture of our world. The quantity of data being collected will increase significantly, with gigabytes of lidar, radar and camera footage acquired every second.
The information generated by a fleet of AVs will present numerous opportunities – real-time journey information, not just speed data, might enable improved network management, which will benefit all road users, including emergency services. Alternatively, live in-dash cameras could be remotely accessed to detect and monitor crime, or inform emergency services as to the required level and scope of assistance. However, a key risk is that the size and nature of the data collected by AVs will be too difficult to interrogate securely and expensive to manage. Organisations looking to extract value from this information must invest in analytics tools and skill sets and also in information security processes and awareness to efficiently capture and process this data for applied use.
We’re on the cusp of a hugely disruptive technology, the impacts of which will permeate our society – movement data and analytics will be at the heart of this innovation.