# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 4 Performance Evaluation
### Performance in a Real Vehicle  
In order to evaluate the inverse method proposed in this paper realistically and effectively, a mid-size sedan manufactured in 2017 was used as the test vehicle, and a representative internal network implementing the standard CAN protocol was employed. This means that the ECU in the CAN network could send and receive telegrams with a data field lengths of 64 bits. In addition, the DBC file for this vehicle was obtained [29] and used to evaluate the ground truth of our method. To obtain the experimental data, we used a combination cable to connect the vehicle's OBD-II interface to the USB interface of a laptop, and we used a script to write the data to a local log file. Following the experimental approach used in this paper, we collected the CAN traffic from the car when it was at a standstill (\(\Omega_{s}\)), at a standstill performing only body operations (\(\Omega_{b}\)), and during daily driving (\(\Omega_{m}\)) in the data collection phase. Quantitative descriptions of the datasets are given in Table 3.  
The CAN traffic data collected from the test car under different conditions were first grouped by ID, and for each ID corresponding data field, the bit flip rate was calculated for the time series. The data fields corresponding to each ID were labeled based on the results of the bit-flip rate. In the actual experiment, we collected 81 IDs from the CAN traffic, of which 8 IDs were filtered and determined to be related to body behaviors based on the labeling results because their data fields contained only B-labeled messages.  
For the filtered CAN message, fuzzing was performed on the bits in each message with only B tags, and the original data from the irrelevant bits were kept to generate the test messages. As shown in Table 4, eight body operations can be triggered by sending a test message. We recorded each message that triggered the body behavior and executed the AND bit-by-bit process. We executed the possible body operations five times for the behaviors that could not be triggered by the test message. We recorded the number of changes in the bits marked as B. A message with five changes was recorded, and the AND bit-by-bit process was executed for messages with a marked bit value of 1. Eventually, we obtained the CAN messages describing the specified automobile body behaviors in the CAN traffic and determined which bits in the data field controlled or indicated those body behaviors. The reverse results are shown in Figure 8. The results show that the CAN messages controlling the automobile body behaviors are concentrated in operations such as lights and wipers. The number of bits controlling these behaviors is small, usually 1-3 bits. Most of the other typical body behaviors, such as pedals, seat belts, and doors, are controlled by the driver, and CAN messages only describe their statuses. The number of data bits characterizing these vehicle behaviors was usually high, e.g., above 4 bits.  
\begin{table}
\begin{tabular}{c c} \hline
**Dataset Type** & **CAN Message Count** \\ \hline Standstill(\(\Omega_{s}\)) & 159,890 \\ Only body operations(\(\Omega_{b}\)) & 605,112 \\ Driving(\(\Omega_{m}\)) & 132,707 \\ \hline \end{tabular}
\end{table}
Table 3: Datasets and message counts.