Self-learning road infrastructure will provide intelligent object-specific early warnings


The Universities of Applied Sciences of Ulm and Heilbronn, Germany, along with industrial partners, have announced the creation of Salus – a mix of radar, optical cameras and infrared sensors plus neural networks with machine-learning, that is designed to be able to differentiate between pedestrians, cars, bicyclists, motorbikes, deer, foxes, wild boar etc – and predict the behaviour of these objects.

The system then can send warnings to car drivers and other road users to prevent accidents. The data of the micro-Doppler radar is gathered by a Spectrum Instrumentation PCIe digitizer card M2p.5926-x4 that provides the required number of channels and bitwidth.

“The aim of our project is to have small installations by the roadside that detect hazards and communicate this to vehicles as they approach,” says Professor Dr. Hubert Mantz from the Ulm University of Applied Sciences, who is one of the leaders in the project. “In addition, for road users without in-car warning displays, road lights could be turned on to highlight the area of the hazard and/or warning notices illuminated. Project Salus detects and warns of the harder-to-see hazards and should significantly improve road safety.”

The technical demonstrator system will be able to measure simultaneous data from three sources – radar, optical camera and infrared camera. Additional sensors could be integrated, for example, to measure pollution levels, which has already generated commercial interest from companies in the project’s consortium.

“We are using neural networks to develop the machine learning that enables the system to differentiate between e.g. bicyclists, cars or deer. This takes it far beyond pure motion detection,” adds Prof. Mantz. “We are at the critical part of the project which is the classification of detected objects, which has never been done before. With this, the system will be able to predict the movements of objects which adds incredibly useful, real-time intelligence to the system enabling it to foresee how a hazardous situation could develop.”

The project envisages wide-scale deployment of these self-contained units as posts beside roads across Germany, which means that they must be inexpensive and solar powered. The latter is particularly important for rural areas where mains electricity is not readily available and the need for such a warning system is greatest as street lighting is scarce.

This also means that the communication system between units to form an intelligent transport infrastructure must be low power so Long Range Wide Area Network (LoRaWAN) is used as it can reach up to 40km (25 miles) in rural areas. This standard is characterized by its low energy consumption, and is based on unlicensed frequency bands and therefore available at low cost.

“We chose the Spectrum PCIe digitizer M2p.5926-x4 with 16-bit, 4 differential channels and 10 MHz bandwidth,” says Prof. Mantz. “It gathers the information to teach the system as it enables us to process all the data we need simultaneously in real time. We have found it to be very easy and intuitive to use which means we can focus on the project rather than programming it. Most importantly, it comes with a five-year warranty so we don’t have to worry about any issues of maintenance and repair unlike other companies’ standard offering of only one year which could mean additional costs if their equipment goes wrong.”

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Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).