For any disruptive technology, targeted at any market, there must be a ‘killer app’: a concrete use-case with a well-defined ROI to dictate investment, M&A, and go-to-market strategies. For autonomous vehicle (AV) technologies, that ‘killer app’ is the Mobility as a Service (MaaS) opportunity. While improving driver safety through better obstacle detection and collision avoidance can be achieved with active safety technology, only a comprehensive AV system can kickstart MaaS into the mainstream, driving down the cost per mile of shared mobility modes to near parity with car ownership.
This is the reason that Intel’s Mobileye has valued the MaaS opportunity at US$160bn by 2030, over three times their estimation of the value of the self-driving system market by the same year, an expectation matched Intel’s US$900m acquisition of Moovit. In its own words (in late 2019), “MaaS will govern the self-driving productisation pace. The consumer AV market will be timed by self-driving system productisation and consequent cost/value optimisation steps within MaaS. Developing MaaS and driving it to quick convergence is critical to secure our self-driving system product fit, and to dominate the consumer [i.e., passenger vehicle OEM] AV ramp up ahead of the industry learning curve.”
Therefore, not only does the nascent MaaS market need AV technology to scale, but the AV technology needs the MaaS ‘killer app’ to mature into the mainstream. Critically, the MaaS paradigm is one that will grow exclusively in the context of cities. Therefore, it is vital that cities be made ready to accommodate AVs, and that AV technology be developed for safe deployment in cities.
Any observer of the AV ecosystem will have become accustomed in recent years to an inbox filled with announcements by AV pod manufactures and municipal or city governments, outlining how a handful of experimental prototypes, equipped with full time backup drivers, will operate a small number of fixed routes on public highways. These engagements have been focused on testing consumer perceptions of AVs, while also identifying any weaknesses in the AV software or hardware components. In order to move out of this testing and prototyping rut, cities can take the following positive steps.
Engage with IEEE P2846: One of the biggest barriers to the rollout of AVs in cities is risk, as driving is a multi-agent problem, involving a variety of decisions and counter-decisions, manoeuvres and counter-manoeuvres by all the road users involved. In the city context, these road users include not only other vehicles but also vulnerable pedestrians and cyclists, moving in multiple directions in scenarios involving various occlusions, etc. It is vital that cities move beyond naïve sentiments of AVs ending all collisions—a distortion sometimes persisted by robotaxi technology developers—and work with AV deployers to develop deterministic safety models.
Such deterministic safety models depend on a number of assumptions and variables, and by working with AV developers to define what these variables should be in their city, city governments can have their own say in defining the trade-off between efficiency and risk in the rollout of AVs. The IEEE P2846 working group builds upon the responsibility-sensitive safety (RSS) model originally developed by Mobileye, and provides city governments with the best opportunity to shape the risk-reward balance of AVs in their own cities without requiring them to become too deeply involved in dictating hardware architectures, software architectures, or any of the other remaining ‘nuts and bolts’ of each AV system that may ultimately end up being deployed in their city.
Digital twins: While deterministic safety models are the best way to provide verifiable evidence of the safety of an autonomous system, simulation tools have also proved useful in the development and testing of AV components, allowing for rapid prototyping in the digital domain. At the same time, many city governments have begun investing in digital twins of their cities in order to boot resilience, helping to identify potential bottlenecks in the event of a widescale evacuation, or the environmental impact of approving a new building to be constructed, etc. Having developed these digital twins, these models can also help AV deployers to prototype and test vehicles in the digital domain on a specific recreation of the actual deployment city. This is particularly relevant in cities with known problem areas, road features which are known to cause complications for all road users.
Clearly elaborate AV objectives: What is each city looking to achieve through the introduction of AVs? What transportation problem areas are cities hoping AVs will alleviate? How would cities like to see AVs incorporated into the current mix of different mobility modes? By clearly elaborating their objectives for AV deployments, city governments can help AV deployers optimise their deployment in their city. For example, in 2019 the UK government published its Future of Mobility: Urban Strategy, which outlined a long-term vision for personal mobility in urban environments. It includes a set of principles for how new mobility modes (such as AVs) should be introduced. For example, emphasising the important of smart mobility modes integrating with public transit modes and giving preference to active transit. Understanding city government expectations for how AVs should operate (outside of typical concerns about risk and liability) can help deployers to build a better business case for deploying into a new city.
In practice, the bulk of the work still required for the successful introduction of AVs into cities is on the part of AV developers. Indeed, the lesson of history suggests that a go-to-market strategy requiring significant initiative on the part of city governments is suspect at best.
For example, the vehicle-to-everything (V2X) market has been held back for over 20 years by limited first mover advantage—vehicle-to-vehicle (V2V) applications require a significant installed base, therefore it was hoped that installation of V2X modules in infrastructure would enable a host of vehicle-to-infrastructure (V2I) applications to encourage adoption of V2X modules in cars. Suffice to say, this market dynamic did not exactly manifest as hoped. Why should cities with stretched budgets commit significant resources to enable an unproven technology for the sake of the automotive industry? If a safety focus on reducing collisions did not save V2I, it can’t be expected that city governments will come to the rescue of AVs on their own initiative and at the taxpayer’s expense.
No, the onus is ultimately on AV developers and deployers to make their systems safe for city deployment, and their AV applications relevant for the transportation headaches common to all cities. Priority number one should be provable safety through deterministic safety models, such as that being developed by IEEE P2846. A probabilistic, black-box approach accompanied with a promise that it definitely works is not going to wash with city governments any longer, and the more AV deployers can involve each city government in the exercise of balancing assertiveness and safety, the easier they will find it to expand their geographic footprint.
About the author: James Hodgson, Principal Analyst at ABI Research, conducts research relating to the field of autonomous driving and smart mobility, with a focus primarily on quantitative forecasting and analysis in the areas of advanced driver assistance systems (ADAS), autonomous driving, and connected infotainment.