Blog

• News
• Smart radar unit
• Data analysis
• CitRad starts at the Prototype Fund
• Retrospektive: Hardware
• First measurements and a lot of interest in our project
• The first prototype
• How CitRad was conceived

News

Smart radar unit

After we have developed an algorithm on the computer to analyze the radar data, the whole thing now has to be implemented on the device. Our goal is that all the data is already analyzed in the box and we only need to store the number of cars and their speed.

Compared to the evaluation on the computer, there are a few things that need to be done differently on the device: On the computer, we worked with recorded data. On the device, we do the analysis live. This means that we have to buffer values in some places. For example, for smoothing the noisy raw signal. In one direction of travel, we have to buffer the speeds until the trigger of a passing car arrives. Only then can we save the data. For cars traveling in the other direction, we receive a trigger and then have to wait for the speed data.

In addition, there is still a lot to optimize in order to evaluate the data efficiently on the small computing core.

You can follow our development in the SensorUnit repository on github.

Data analysis

After all our experiments with amplifier circuits and a low-noise power supply, our sensor now seems to be working well. At least you can clearly see different vehicles in the spectral images of the radar data and recognize how fast they are driving. But how can the whole thing be evaluated with an algorithm? In the end, we just want to count vehicles and save the speed of each vehicle.

The eye can easily distinguish the actual signal from the noise. In data analysis, this requires some smoothing and well-chosen limit values. The noise also changes over time. When the battery discharges. We also had to find a solution for this.

The signal analysis is based on the fact that certain frequencies stand out from the spectrum. This can be seen as lines over time. Each line is a moving object. A moving car, for example. The speed can then be calculated from the frequency.

We use a special trick to recognize when a car passes by: As the car passes, the angle to the Rarsensor changes. This causes the signal to bend downwards (towards 0 km/h). We recognize this bend and know that a car has just driven past the sensor. Whether this bend is to the left or to the right tells us in which direction the car was traveling. Now we just have to determine the detected speed before or after this trigger and save it.

What sounds simple is the result of a lot of fiddling around. And of course we first developed the whole thing on the computer using recorded data. This now has to be programmed and tested on the device itself. Only then will we be ready to give the device to interested parties for their own measurements. But that will be soon!

If you are interested in the technical details, you can take a look at our repository on github.

CitRad starts at the Prototype Fund

After the hardware was developed the year before, CitRad is one of the funded projects of the 16th round of the Prototype Fund Software. The funding from the Federal Ministry of Education and Research enables us to improve the sensor’s evaluation routines and work on a data platform. The funding period runs until February 2025.

Following the development of the hardware and the initial tests, we took the opportunity in 2024 to present CitRad at district festivals and events such as the car-free university day and Parking Day. The response was consistently positive. We also presented CitRad to the city of Cottbus in spring 2024. It quickly became clear that there was a basic level of interest from a wide range of parties - from private individuals to mobility research and urban development.

Successfull application to the Prototype Fund

It quickly became clear that we wanted to continue working on CitRad. The fact that the application phase for the 16th round of the Prototype Fund had just started played into our hands. So we had to write an application and hope. But not for too long. Soon the invitation to the kick-off event on September 1 in Berlin fluttered into our mailbox.

CitRad is now one of 25 open source projects that will be funded for six months. Six other projects can also be assigned to the topic of city/mobility. We are excited to see where we will find intersections and look forward to an exciting next six months

Retrospektive: Hardware

First measurements and a lot of interest in our project

With an initial prototype that provides data, we were finally able to invite interested parties to take measurements. At the same time, we took the opportunity to present the project at thematically appropriate events such as the Car-Free University Day or the Parking Day in Cottbus.

Measuring under live conditions

In order to collect initial comparative data, but also to pick up interested citizens, we were drawn to the roadside relatively quickly. Using our own bicycles and cargo bikes, we generated our own trajectories and were able to compare them with “normal” road traffic. Even here, important patterns emerged that were crucial for the subsequent evaluation routine.

CitRad makes itself known

To make CitRad better known to the city’s population, we also ventured to various city festivals. For example, visitors to the Karlstraßenfest were able to experience their own radar signature as a sound output.

At more topic-specific events such as the Car-Free University Day, we were able to exchange ideas with mobility researchers from BTU Cottbus-Senftenberg and exchange initial ideas on where and how CitRad can be particularly effective.

We were also represented with CitRad at Parking Day 2023. This event is dedicated to critically examining the space that cars take up in urban areas through parking alone and how this can be used as an alternative to improve the quality of life in cities. CitRad also met with an interested audience here. Our approach of bringing perceived truths back to facts was consistently perceived as an important first step.

The first prototype

The CitRad sensor had to be based on a radar sensor that was as inexpensive as possible but still reliable and easy to replicate. After a few tests, we chose the IPS-354 module from Innosent. We also needed a powerful microprocessor to process the signals from the sensor. Here we opted for the Teensy 4.0 in combination with the Teensy Audio Board, which is also available. The signal from the sensor can be processed with audio tools. The Audio System Design Tool and detailed Tutorials are available for this purpose. We were particularly interested in the fact that a spectral transformation (FFT) can be implemented. In contrast to other systems, the Teensy can continue to record data while it is doing the data processing so that we do not lose any data.

A low-noise amplifier?

Initially, we were still convinced that we needed an amplifier circuit to be able to use the signal from the sensor. We tested various amplifier boards and even designed a completely custom board. In the end, however, we realized that we wouldn’t need any amplification at all if we used the 32bit Audio Library, which uses the full bandwidth of the digital converter on the audio board.

First tests

With a few jumper cables and connectors, we were now ready to assemble the very first prototype. Since laptop operation was planned at the beginning anyway, all components were supplied with power via USB cable and at the same time a data connection was established so that the data could be viewed. The results looked very promising!

How CitRad was conceived

Where do you start to make a meaningful contribution to the traffic turnaround? We pondered this for a few days at FabLab Cottbus. We quickly realized that we wanted to implement a joint project. With the participation of interested citizens from Cottbus and the surrounding area, a citizen science project was to be created that would collect traffic data and make it available to everyone for evaluation and, above all, as a basis for discussion on traffic-related decisions in the city of Cottbus.

It was not yet clear to us at the time how we wanted to collect this data. Our first thoughts were along the lines of AI-evaluated video images. Although this would not have been a technical problem, it would have raised unanswered questions and caused problems in terms of data protection.

Radar instead of video

It was a good thing that the FabLab brings together people with a wide range of knowledge. One of them had the necessary prior knowledge and the brilliant idea of using radar signals instead of video images. In one fell swoop, all data protection concerns were dispelled and the research for the best components for an initial prototype began.

Finding a name made easy

Every good project also needs a good name. Since we wanted to stick to the idea of citizen science and the radar system was now the focus, we quickly agreed on an acronym of Citizen Science Traffic Radar.