In recent years, some bus companies have raised revenue by reviewing the route plan using the number of passengers. The company has a system that can automatically counts the number of passengers on an ongoing basis. But they are costly because they use cameras and sensors those are dedicated for counting. It is too expensive for bus companies that really need to reconsider their route planning to introduce the system. In order to solve this problem and realize efficient operation, we propose a method to count passengers by using a drive recorder and sensors those are already equipped with buses. Drive recorders and various sensors will be obliged by the government to be set up by bus operators in the future. We constructed a model using Random Forest Regression with the position of the bus from the GPS module in the buses, the position of the bus stop used for operation management, and the number of passengers estimated from the image processing method combining YOLOv3 and Deep SORT. As a result, the average correct answer rate when the passengers get on and off are 96.2% and 70.1% respectively. Our method which utilized non-dedicated camera achieved higher correct answer rate than the conventional method which utilizes dedicated camera for counting passenger.

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CAN uses no authentication and encryption mechanisms for secure communication. To solve the security issues of the CAN bus, a deep learning-based intrusion detection systems have been proposed. But due to the high dimensional property of the CAN bus data, it was not possible to create an effective Intrusion Detection System (IDS) in the CAN bus that can take the property of the CAN data into consideration. In this paper, we are proposing a Long Short-Term Memory Networks (LSTM) based IDS that can handle the high dimensional property of the CAN bus data . Unlike the conventional methods which required a single network architecture for each unique arbitration ID, our method gives a single overall anomaly signal over a certain detection window without the need for reverese-engineering the CAN bus data. Using this anomaly signal we have managed to achieve 100% detection precision for insertion, fuzzy and targeted attacks in our data and in a public data that is prepared for this specific purpose.

The number of computer controlled vehicles throughout the world is rising at a staggering speed. Even though this enhances the driving experience, it opens a new security hole in the automotive industry. To alleviate this issue, we are proposing an intrusion detection system (IDS) to the controller area network (CAN), which is the de facto communication standard of present-day vehicles. We implemented an IDS based on the analysis of ID sequences. The IDS uses a trained Long-Short Term Memory (LSTM) to predict an arbitration ID that will appear in the future by looking back to the last 20 packet arbitration IDs. The output from the LSTM network is a softmax probability of all the 42 arbitration IDs in our test car. The softmax probability is used in two approaches for IDS. In the first approach, a single arbitration ID is predicted by taking the class which has the highest softmax probability. This method only gave us an accuracy of 0.6. Applying this result in a real vehicle would give us a lot of false negatives, hence we devised a second approach that uses log loss as an anomaly signal. The evaluated log loss is compared with a predefined threshold to see if the result is in the anomaly boundary. Furthermore, We have tested our approach using insertion, drop and illegal ID attacks which greatly outperform the conventional method with practical F1 scores of 0.9, 0.84, and 1.0 respectively.

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