TRAFFIC g
an object, for example, it can take into account the geometry of the object using 3D vision, without being dependent on stable illumination – the 3D camera is robust to changes in lighting. ‘Real-time processing is important, so we
combine both shallow and deep learning in order to get the performance, both in terms of accuracy and the real-time nature of the system, without having very expensive hardware,’ Singh explained. Te system runs on a Linux computer together with a GPU. Te raw data output from the camera
is tracking data at 20Hz, which contains information about the type of road user, the position, the time, and additional metadata. At Intertraffic, Viscando will show its
traffic monitoring capability, as well as how the cameras can be used for real-time control based on vision data. Swisstraffic will also present a new vision
system at Intertraffic with AI built in. Similar to Viscando’s camera, the Swiss AI sensor is able to evaluate images in real time and provide vehicle, bicycle and pedestrian tracking onboard the camera. It’s also GDPR- compliant, as there’s no image stored and there’s no video streaming. Te AI software gives 95 per cent accuracy. Te AI camera has been installed
on roadways in a trial project in Bern, Switzerland, conducted with Swisstraffic’s partner Elektron, which makes smart lighting systems. Te camera was used, among other things, to control streetlamps depending on the volume of traffic – when there’s no traffic on the road, streetlamps can be switched off to save energy and reduce light pollution. ‘Machine learning offers us a huge
opportunity for traffic management,’ commented Maria Riniker, chief communication officer at Swisstraffic. ‘Adding these sensors could make road junctions safer. Te device can detect near- misses between road users, for example; these kinds of statistic are not normally recorded.’ Swisstraffic showed the AI system for
the first time at the Smart City Expo World Congress in Barcelona last year, and now at Intertraffic Amsterdam in April. Swisstraffic’s Alain Bützberger will present the firm’s technology at Intertraffic on 22 April, summit theatre two, 2pm. Vitronic is also building AI into its
automated toll collection systems, which will be presented at Intertraffic Amsterdam. By using a convolutional neural network alongside classic image processing, Vitronic engineers were able to improve recognition rates for reading number plates, as well as classifying vehicles, according to René Pohl, product manager for tolling at Vitronic. He said it is particularly important for
Visualisation of the movements of road users in Umeå. Besides counting the tracking over multiple counting lines, Viscando also analysed the tendency for traffic conflict
‘Te [vision] device can detect near misses between road users. Tese kinds of statistic are not normally recorded’
customers that every vehicle is detected and recognised; otherwise they will lose revenue. Vitronic is also working with the
University of Applied Sciences in Darmstadt, Germany, where researchers are using data from existing and new traffic enforcement and management infrastructure, combined with environmental data, either as an empirical database for static traffic models, or as input data for dynamic traffic models.
Autonomous driving Te tracking data Viscando generates is also valuable for the automotive industry, both for traffic safety and for autonomous vehicle development. ‘We are lucky enough to be situated in Gothenburg, where Volvo Cars and Volvo Trucks have their headquarters, along with Volvo’s suppliers Veoneer, Zenuity and Autoliv,’ said Singh. Tose suppliers all develop technology for ADAS and autonomous driving. Viscando has a good collaboration with
Volvo, Veoneer and Zenuity, as well as Ericsson. ‘Tese companies use our data to understand how road users behave and interact, so they can consider how
18 IMAGING AND MACHINE VISION EUROPE FEBRUARY/MARCH 2020
autonomous vehicles should be developed and what kind of situations they will end up in,’ explained Singh. He went on to say that an autonomous
vehicle is a safety-critical system, and because its operation is based on machine learning, there is going to be some kind of regression involved. ‘All machine learning methods tend to concentrate on main features and remove any outliers,’ he said. ‘But for safety-critical systems, you can’t really remove the outliers because, in most cases, those are very important.’ Viscando collects data that identifies
the type of outliers that road users might encounter in different situations. Automotive firms can then take that into account when simulating and framing their algorithms for autonomous driving. Te data means that driving simulations can be based on real-world data for 95 per cent of the movement of vehicles, with only the last 5 per cent extrapolated, according to Singh. Viscando has a three-year project with
Volvo Cars, Veoneer, Autoliv and Chalmers University of Technology, where Viscando cameras will be collecting data for some parts of the project. Viscando will be expanding a lot this year,
Singh said. It will offer a real-time traffic control system for small intersections by the middle of 2020. Te firm can already deliver a system for controlling cycle paths. Viscando is also working on making some of the analysis functionality that’s currently run offline available as online, real-time functionality. O
@imveurope |
www.imveurope.com
Viscando
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