How AI can help cities to better manage transport
*The following Cities Today article has been reposted with the publisher’s kind permission. The original article can be found here.
Artificial intelligence (AI) applications in the transport sector are stimulating innovations for better and more targeted use of vehicles and infrastructure. This could optimize network performances, support the monitoring and management of traffic, and create the base for solutions that pave the way to future mobility, particularly in cities. Infrastructure management and vehicle design are evolving thanks to the opportunities offered by the widespread use of devices, such as smartphones and in-vehicle localization sensors, for processing, gathering and exchanging information among users and service providers, as well as for monitoring and detecting the performance of vehicles and behaviour of people. Altogether these create a huge amount of data (big data), which is the primary source for using AI in transport, allowing computers to perform activities for humans, such as driving. Levels of automation The most advanced and revolutionary AI application in transport today is the automation of vehicles. The classification most used to describe the degree of automation is defined by standards from the International Society of Automotive Engineers (SAE). This provides six levels of automation (including Level 0, which means there is no automation), identified according to who (i.e. human driver or system) performs the operation and at what time. Today’s cars are generally equipped with SAE level 1 and 2 features, commonly referred to as Advanced Driver Assistance Systems (ADAS), such as park assist, cruise control, adaptive front lights and lane keeping assist (see figure 1). These devices support drivers in terms of providing aid, warning and assistance, rather than replacing them in driving activities (full automation). In addition, a few vehicle manufacturers can offer models furnished with partial automation functions, such as autopilot under certain conditions and autonomous valet parking, but drivers are required to be in control of the vehicle at all times (SAE level 3). In future, leading car manufacturers, as well as newcomers in the automotive industry such as Google, are expected to commercialize fully automated vehicles (AVs), which will be able to act as an intelligent agent and to adapt to their context using data collected in real-time from cameras, Light Detection and Ranging (LiDAR) systems, localisation sensors and digital maps.
Views expressed in this article do not necessarily reflect those of ITU.
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