Abstract:
:Detection of lane-change behaviour is critical to driving safety, especially on highways. In this paper, we proposed a method and designed a learning-based detection model of lane-change behaviour in highway environment, which only needs the vehicle to be equipped with velocity and direction sensors or each section of the highway to have a video camera. First, based on the Next Generation Simulation (NGSIM) Interstate 80 Freeway Dataset, we analyzed the relevant features of lane-changing behaviour and preprocessed the data and then used machine learning algorithms to select the suitable features for lane-change detection. According to the result of feature selection, we chose the lateral velocity of the vehicle as the lane-change feature and used machine learning algorithms to learn the lane-change behaviour of the vehicle to detect it. From the dataset, continuous data of 14 vehicles with frequent lane changes were selected for experimental analysis. The experimental results show that the designed KNN lane-change detection model has the best performance with detection accuracy between 89.57% and 100% on the selected dataset, which can well complete the vehicle lane-change detection task.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Du L,Chen W,Pei Z,Zheng H,Fu S,Chen K,Wu Ddoi
10.1155/2020/8848363subject
Has Abstractpub_date
2020-09-30 00:00:00pages
8848363eissn
1687-5265issn
1687-5273journal_volume
2020pub_type
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