# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 2 Background and Related Work
### Related Works  
Whether monitoring abnormal CAN messages, studying the vehicle's operating status, or implicitly authenticating the driver, it is essential to have comprehensive and accurate information on each bit of the CAN message's data field. This is done through translation. Currently, there are three types of reverse methods that can be used for the CAN message's data field. Figure 1: Structure of CAN frame. The first method type is based on the data variation characteristics of each bit. For example, [PERSON] et al. extracted the signals and their boundaries by observing the data variation pattern of each bit of the data field corresponding to the same ID over time [14]. [PERSON] and [PERSON] proposed an algorithm to calculate the bit-flip rate and the magnitude array for each bit of the data field for each ID in order to determine the data field signal boundaries [15]. [PERSON] et al. optimized the field segmentation algorithm based on the study by [15]. They matched the correlations between the segments with the help of OBD-II diagnostic and smartphone sensor data, which led to a more accurate classification of the field segmentation [16]. However, these methods only classify at the segment level and are not specific to each bit. Moreover, they do not form a mapping of the segments in response to specific events, which makes the results less valuable. The second approach is implemented based on diagnostic protocols. For example, [PERSON] al. located the exact bits by matching the return value of the OBD-II diagnostic protocol with the data in the CAN message [24]. [PERSON] et al. improved on the study conducted by [24]. They made two data structures by simultaneously using the results obtained with OBD-II diagnostics and the CAN bus data. They used the Pearson correlation coefficient to determine the correlations and thus locate the valuable bits [20]. [PERSON] et al. used algorithms to extract CAN message signals and label them with the help of OBD-II PIDs, thus enabling mapping from bits to ECU functions [25]. However, these methods are limited by what the diagnostic protocol can provide. The diagnostic protocol can only provide a small amount of information about the motion status and very little about the vehicle body. The third method type is implemented with the help of machine learning techniques. For example, [PERSON] et al. and [PERSON] et al. identified multiple CAN message features as being needed for machine learning and trained their models with them, thus making their models capable of classifying CAN messages [17; 26]. However, the CAN message features used to train machine learning models need to be defined manually, and the number of classification categories they could eventually identify is limited. In our previous work, differential-based [27] and linear-regression-based [21] CAN message reversal methods have been successively proposed. The first proposed differential-based scheme finds the control bits of the car operation by performing a differential on the CAN datasets collected when the car is stationary and dynamic. Although this approach enables bit-level CAN message reverse, it is inefficient and inaccurate. In 2022, a multiple-linear-regression-based reverse framework was proposed, which built a multiple linear regression model between sensor data and CAN message data bits and used the model parameters to locate the bits controlling the vehicle's behavior quickly. This approach allows for the inversion of vehicle behaviors with higher efficiency and accuracy by only collecting data once. However, as the sensor data only reflects the behavior related to the vehicle driving or performing an operation (e.g., vehicle speed, acceleration, pedal angle), it causes this method to fail for CAN messages describing the state of the vehicle (e.g., lights and doors). The complete DBC file defines the content of each valid message in the CAN network.