Using SICK sensors, artificial intelligence makes the roads safer in the San Bernardino tunnel
Can artificial intelligence make road tunnels safer and more efficient? The Vehicle Hotspot Detection Systems (VHD) from SICK at the northern and southern entrance of the San Bernardino, Calif. tunnel answer this question with a resounding “Yes!”
Using deep learning, AI has been trained to distinguish hazardous overheating hubs on vehicles from hot but not dangerous mufflers. The result: higher safety with fewer false alarms and optimal traffic flow.
“The VHD systems from SICK are used to detect trucks and buses with overheated surfaces, such as surfaces at risk of combustion before they drive through a tunnel, onto a ferry, into a loading terminal, or into a traffic checkpoint,” said Lukas Wallimann, Product Manager VHD at SICK in Buochs, Switzerland. The system performs this task by combining images from infrared cameras with the measurement data from 2D LiDAR sensors.
This enables the system to measure temperatures on the vehicle and determine its dimensions to create a 3D model on which individual vehicle areas such as the wheels, motor, exhaust, or load compartment can be automatically identified.
“These kinds of systems have been installed throughout Europe during the last decade, including on the Mont Blanc tunnel and on the Gotthard tunnel,” said Christoph Gilgen, Customer Project Manager at SICK.
VHD systems have proven in practice to be a reliable safety measure against vehicle fires in tunnels.
“The thermal portal on the San Bernardino helps us remove overheated vehicles from the traffic thereby contributing to tunnel safety,” said Italo Broggini at the Bellinzona branch of the Swiss Federal Roads Office (ASTRA).
The thermal portal on the San Bernardino helps to remove overheated vehicles from traffic, thus contributing to tunnel safety.
Room for improvement with alarm accuracy
So the VHD systems are effective, but to make them even more efficient, it was necessary to further improve the quality and accuracy of the alarms. This was an essential prerequisite for the San Bernardino project because the rescue service for the tunnel is only located at the southern portal.
If the thermoportal raises an alarm at the northern entrance to the tunnel, which is about seven kilometers (4.3 miles) away, it will take emergency personnel some time to get to the hazardous vehicle. False alarms, such as being triggered by a hot muffler, lead to unnecessary deployments of the rescue service. But how much room for improvement on alarm quality do the VHD systems have – and how can this potential be exploited for greater efficiency as well? In comes artificial intelligence!
Artificial intelligence improves the alarm accuracy of the VHD
The system is accurate, but how can it be improved? Simply by using more sensors? Or are new algorithms required to enable potential ignition sources on vehicles to be more reliably identified and distinguished?
“The solution can be found in artificial intelligence, or more precisely deep learning,” said Roman Schindler, CNN Software Engineer at SICK.
This “CNN” we’re referring to has nothing to do with the news channel, but instead stands for Convolutional Neural Network – an artificial neural network that is inspired by biological processes and is predominantly used for the computer processing of image data. A particular advantage of this method is its low error rate, which it was hoped would be confirmed when using VHD systems with CNN algorithms.
In the so-called “training camp” of the VHD system, thousands of thermal images were analyzed over four months, thereby training the CNN algorithm.
Learning by training, learning from images
The VHD systems spent four months in a training camp.
“During this time, thousands of thermal images of existing systems were collected, analyzed, manually classified, and then used to train the CNN algorithms,” Schindler said, describing the development process.
Furthermore, SICK went through all the alarms during the past few months with the captain of the San Bernardino fire and rescue team to filter out the actual false alarms among all of the alarm notifications. This measure was also useful for the CNN algorithm. The new method significantly improved the system’s ability to differentiate between individual areas of the vehicle such as wheels, motor, or exhaust.
“At the San Bernardino tunnel, where the new algorithm was tested in real-time operation for the first time, a lower false alarm rate established itself,” Gilgen said. “This is a more than significant improvement compared to the conventional algorithm, which is also still running there at the thermoportal in the background as a backup.”
Overheated vehicles are taken out of service.
CNN algorithm: upgrade of existing portals
“Thanks to the better false alarm rate, there are fewer unnecessary deployments, which results in cost savings and increased motivation of the personnel in the event of an alarm,” Broggini said.
No wonder then that the operators of existing VHD systems are fired up about upgrading their systems.
“Any one of these systems can be retrofitted with the CNN algorithms because it only involves a software update,” Wallimann said. “Newly ordered VHD systems will be equipped with this upgrade right away. The customer thereby benefits from SICK’s longstanding experience in the thermal measurement of vehicles without having to pay more for the upgrade than the conventional algorithm.”
VHD systems never stop learning
The current release version of the CNN algorithm is not the final one because the system performance will continue to improve with new image data and training cycles.
“This means VHD systems never stop learning,” Schindler said.
Furthermore, new and related fields of application are always opening up for these systems. For example, SICK is working on a further development of the VHD system for ferry terminals to be able to detect hot spots on the refrigeration units on trucks.
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