TRANSPORT


‘What more is possible? Can AI be used to predict traffic and road user behaviour?’


more important than kilometres,’ said Singh. Both quality and quantity of data


are required. Singh believes that use of the passive data captured on camera infrastructure could be used here, especially as they observe real-world situations and more complex interactions. ‘We can collect billions of kilometres passively,’ he said.


Talking to the city Image processing onboard the camera, close to the sensor, opens up the potential to capture more data that is ultimately going to make cities or transport smarter. NTT Smart Solutions has a focus on Internet of Tings-enabled edge analytics, and vision is one important input into its systems. Current clients use NTT solutions to


predict, for example, train occupancy 24 hours ahead, or the movement of people into and around large venues for crowd control, with a 20-minute forward horizon. NTT is developing the potential of


before they act; to predict if, say, a bike or pedestrian will cross the road or continue along it up to five seconds before the decision is made. ‘We are not at 100 per cent accuracy, but 82 per cent,’ he said. ‘Te data is there; we just need to be better at extracting it.’ Tere is plenty to do from the tech side in


terms of pure data analysis. ‘It’s very much in line with what you need to have if you are applying machine learning,’ commented Singh. ‘Tere is room for other disciplines to contribute here, such as with behavioural insights, but the data on what the pedestrians and cyclists are doing is already there and the information available.’ Tis leads to two entangled loops in terms


of decision-making. ‘Tere is a low latency for decision-making in traffic. You have perhaps 50 to 100 milliseconds to make a warning and ensure appropriate actions are taken to avoid an accident. Tis may require


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‘Te data is there; we just need to be better at extracting it’


more intelligence in the vehicles, and local processing of data and decision-making in the local infrastructure. ‘And there is a longer loop using the full


data collected to extract insights,’ he added. Tese insights could, for example, help design intersections and crossings to reduce conflict between users. Te same data could be used to generate


scenarios for autonomous vehicle testing. To prove the safety of autonomous vehicles will require billions of kilometres on roads, but that data may not be useful for understanding more complex interactions, such as urban traffic, lane merging and other potential conflict scenarios. ‘Interactions are


connected vehicle systems, working with Toyota to investigate how vehicles can communicate with infrastructure, such as data centres and 5G networks. Currently, these research systems can detect an obstacle and warn a moving car within around five seconds. Other key applications of such a connected vehicle infrastructure include generating accurate, real-time maps and rapid detection of congestion. Bill Baver, vice president of NTT Smart


Solutions, noted that optical sensors will need to be capable of doing some of the analytics within the device itself. ‘We don’t want to be pushing video back to the core data centres,’ he said. ‘Te vision side should have more configurable capabilities. Tis would be helpful for multiple use cases.’ He also argued that imaging technologies


need to be adaptable to multiple data highways and provide application programming interfaces to integrate easily into solutions. With vehicles now being developed


incorporating a battery of sensors – lidar, radar and image sensors across the visible and infrared – it’s only a matter of time before vehicles start to communicate with the city streets they are driving through. O


APRIL/MAY 2022 IMAGING AND MACHINE VISION EUROPE 21


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