Among them, the advanced driver assistance system (ADAS) has received widespread attention. There are different paths for AVs to develop. Driven by cutting-edge technologies such as sensor technology and artificial intelligence, the development of AVs has entered a new stage. The results show that based on the acceleration features, the classification accuracy of lane keeping (LK), lane changing to the left (LCL) and lane changing to the right (LCR) is 100%, 97.89% and 96.19%.Įmerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. The trained model was applied to another set of data and received good results. The normalized features are then input into the k-nearest neighbor (KNN) classification model. Acceleration and velocity are extracted and labeled as physical data. The driving scenario from the natural vehicle trajectory dataset (i.e., HighD) is used for machine learning. This study proposes a model of lane changing maneuver recognition based on a distinct set of physical data. However, models which require a lot of physical data as input and unaffordable sensors lead to the high cost of AV platforms. With the development of technology, machine learning has been introduced in this field with effective results. The lane changing maneuver recognition has been used to study traffic safety for many years. ![]() It is very important for ADAS technology to identify them effectively. Lane keeping and lane changing are two basic driving maneuvers on highways. The advanced driver assistance system (ADAS) has received widespread attention. Emerging autonomous vehicles (AVs) have the potential to significantly reduce crashes. ![]() The increasing number of vehicles has caused traffic conditions to become increasingly complicated in terms of safety.
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