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A platform for research: civil engineering, architecture and urbanism
Lane Change Detection Using Naturalistic Driving Data
Lane Change (LC) detection is the foundation of LC studies using real-world data. Most current studies use rule-based methods for LC detection from experimental data. In this study, we propose a learning-based method to detect LC using large-scale naturalistic driving data. The dataset is analyzed using big data analytics method, and the potential LC maneuvers are extracted. The LC detection is reformulated as a one-class classification problem, and an autoencoder-based anomaly detection method is developed to solve it. The proposed method is robust to data noises and can achieve better detection performance than the one-class Support Vector Machine (SVM). This work lays the groundwork for future LC studies, such as driving behaviour modeling and traffic safety solutions.
Lane Change Detection Using Naturalistic Driving Data
Lane Change (LC) detection is the foundation of LC studies using real-world data. Most current studies use rule-based methods for LC detection from experimental data. In this study, we propose a learning-based method to detect LC using large-scale naturalistic driving data. The dataset is analyzed using big data analytics method, and the potential LC maneuvers are extracted. The LC detection is reformulated as a one-class classification problem, and an autoencoder-based anomaly detection method is developed to solve it. The proposed method is robust to data noises and can achieve better detection performance than the one-class Support Vector Machine (SVM). This work lays the groundwork for future LC studies, such as driving behaviour modeling and traffic safety solutions.
Lane Change Detection Using Naturalistic Driving Data
Guo, Hongyu (author) / Xie, Kun (author) / Keyvan-Ekbatani, Mehdi (author)
2021-06-16
5449475 byte
Conference paper
Electronic Resource
English
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