Enhancing Road Safety with In-Vehicle Network Abnormal Driving Behavior Detection

This study delves into leveraging Controller Area Network (CAN) data to detect and analyze abnormal driving patterns, underlining its significant role in bolstering road safety measures. By meticulously examining the comprehensive data supplied by the CAN system, which encapsulates real-time inputs from many vehicle sensors and mechanisms, this research marks a pivotal stride in the domain of vehicular safety and intelligent transport networks. The investigation elucidates on categorizing three specific types of unusual driving conduct, showcasing the accuracy and dependability of utilizing CAN data for such purposes. This methodology is a critical breakthrough in crafting instantaneous monitoring systems for erratic driving behavior, aiming to foster safer driving environments.