Before revision:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97 \% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal and is the state of the art. In evaluation on ten vehicles of different makes, CAN-D's average \ell^1 error is 5 times better than all preceding methods and exhibits lower average error even when considering only signals that meet prior methods' assumptions. Finally, CAN-D is implemented in lightweight hardware allowing OBD-II plugin for real-time in-vehicle CAN decoding.
After revision:  In addition, we provide two novel, state-of-the-art signal boundary classifiers (both superior to previous approaches in precision and recall ) and the first signedness classification algorithm , which exhibits >97