Abstract
Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.
Highlights
Advancements in the automotive and semiconductor industries have led to the development of increasingly sophisticated vehicles
There has been a recent interest in smart vehicle technologies such as advanced driver assistance systems (ADASs) and autonomous driving, and studies have been conducted on various related technologies [1,2]
We present a novel system for the automatic extraction of vehicle information by analyzing controller area network (CAN) frames collected from the On-Board Diagnostics (OBD) port of the vehicle
Summary
Advancements in the automotive and semiconductor industries have led to the development of increasingly sophisticated vehicles. Vehicle components that can be used to acquire information without additional sensors include the On-Board Diagnostics II (OBD-II) connector, with pins for controller area network (CAN). A single CAN frame can hold up to 64 individual signals Another example of an in-vehicle network is the automotive ethernet-based communication. The SAE J1979 standard defines a method for requesting vehicle information based on the OBD-II Parameter ID (OBD-II PID). Information such as the engine speed (RPM), vehicle speed, and throttle position can be acquired through a predetermined protocol. Unlike conventional methods that require final analysis by experts, our system has the advantage of enabling automatic extraction of vehicle information when driven without human intervention. Evaluations using naturalistic driving data are conducted, and the results demonstrate the feasibility of our approaches
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