Embedded sensors are key to smart mobility growth - Embedded.com

Embedded sensors are key to smart mobility growth

With mobility-as-a-service (MaaS) considered a key element of smart mobility, the one big factor that will be critical to growth is robotic vehicle technology, which in turn will be highly dependent on  embedded sensors.

In this context, high end sensor technology and raw computing power will be at the center of this ongoing market disruption, and sensors for robotic vehicles will become industries of their own, with 51% CAGR expected over the next 15 years, according to market research and strategy consulting company Yole Developpement (Yole). In a new report, Sensors for Robotic Mobility 2020, the firm said sensors are expected to generate US$900 million in revenues by 2024, US$3.4 billion by 2028 and reach US$17 billion by 2032, a time when a million robotic vehicles may be roaming our streets.

The 2024 sensor revenue figure breaks down into US$0.4 billion for lidar, US$60 million for radar, US$160 million for cameras, US$230 million for IMUs and US$20 million for GNSS devices. The split between the different sensor modalities may not stay the same over the next 15 years.

Robotic vehicle sensor system revenue forecast
Expected robotic vehicle sensor system revenues to 2032 (Image: Yole Developpement)

So what are the challenges that Yole sees in the growth of smart mobility? It said current means of mobility are hitting five major limitations. The first concerns the most vulnerable modality, namely that pedestrian safety is deteriorating. Second, in the major cities where people tend to live nowadays, public transportation is facing challenges in terms of efficiency and cost. Third, cars are no longer the grand solution to mobility they used to be. Congestion and cost of ownership is undermining this option. Four, air mobility is currently enjoying rapid expansion, but travel remains difficult as city to airport connections remain poor. Fifth, CO2 emissions due to all current means of mobility make urgent change vital. Regulators and customers are willing to change in both top-down and bottom-up manners.

Yole’s principal analyst, Pierre Cambou, suggests the mobility industry will have to adapt, and for some this will be a massive opportunity. He said, “In this respect robotic mobility clearly checks all the right boxes. Whether it is robotic cars, shuttles or electric VTOL aircraft, the combination of all these new modalities will provide “MaaS” from inner cities, from cities to suburbs and cities to cities. Previous means of mobility will not disappear, just as cinema still existed while TV was massively deployed. Regardless of the naysayers, robotic vehicle technology will provide the Netflix of mobility before 2032.”

He added, “Disruption is coming to our streets and cities. Mobility has defined the way humans have organized their society for ages and our world is currently being reimagined around a new generation of robotic vehicles.” The companies that are most attracted by the MaaS market, which is expected to reach a value of US$2.4 trillion within the next decade, are companies like Google, Baidu, Amazon and Uber. With an additional US$1.1 trillion to be generated by sales of personally-owned autonomous driving vehicles, the added value of autonomous driving suggests a total value of US$3.5 trillion by 2032.

Yole said that robotic vehicles do not focus on the cost and long-term reliability issues that are the main concern for other automobiles. All that matters is the immediate availability, performance, and supportability of their sensor suite. The robotic sensor data flow is limited by downstream computing power. While previous generations were in the range of several hundred Tops (tera operations per second), the latest robotic vehicles are in the range of a thousand Tops. This gives limited increases in terms of sensor data flow, which relates to what Yole calls “More than Moore’s law”. The computing power needed increases with the square of data flow input. The number of sensing cameras, radars and lidars will grow far slower than the performance of robotic vehicle computers.

The way around data sparsity is for roboticists to use “better” data, meaning sensors which bring other types of information. The quality of information is increased, not the quantity. On top of industrial grade cameras and radars, they are massively using 3D sensing lidars, navigation grade GNSS devices and IMUs and more recently thermal IR cameras.

Innovation scenarios for automotive sensing and computing
Future innovation scenarios for automotive sensing and computing (Image: Yole Developpement)

Market and revenue forecasts in the report indicate that 2020 will be the year of industrialization of initial robotic vehicle fleets. For the manufacture of the initial fleets, the report predicts that spending on sensing equipment will hold the highest share at 36% of total cost. By 2032 sensing equipment spend will still represent 28% of total capital spend on robotic vehicles hardware. The use of solid state and the benefit of technology scaling will help lower the price of sensing equipment while at the same time performance of this equipment will rise. In 2019, Yole forecasts made the assumption of a $200,000 robotic vehicle, by 2032 the total robotic vehicle cost will decrease toward $124,000.

 

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