E!10492 RaSCAL
| Reference number | |
| Coordinator | Kvaser Aktiebolaget |
| Funding from Vinnova | SEK 1 647 000 |
| Project duration | September 2016 - September 2018 |
| Status | Completed |
| Venture | Eurostars |
Important results from the project
The goal was to get a tool that first of all could find faults in a CAN-communication. The second goal was to classify the faults and location of the faults. The third goal was to include some kind of AI (Artificial Intelligence) that could do this automatically to help ordinary users to solve the problem. The first goal is done beyond expectation because the measurement can even detect very small changes like changing cable length or adding or removing devices from the communication. The location of the fault has some uncertainty and we still need some manual evaluation to take correct action.
Expected long term effects
We have got a tool that can take a fingerprint of a system and with this as reference it is possible to detect even small changes like cable length or replacement of the connected units. It is also possible to detect if the CAN-frame is sent from a different unit that normal. This makes it possible to protect any vehicle that uses CAN-communication from intentional or unintentional modification. The problem from here, is to have a system that utilize this information in an efficient way to secure that the vehicle is protected but still available for the purpose of the vehicle.
Approach and implementation
Kvaser is a hardware company and most of the project planning was about how to provide the hardware for the project. We soon found that most of the work was software that was more extensive than expected. For that reason have we spent a lot more on the manpower to produce software and less resources for the actual hardware.