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Prediction of Pedestrian Risky Level for Intelligent Vehicles

In this paper, a Pedestrian Risky Level Prediction (PRLP) model based on the data collected by a vehicle-mounted sensing system is proposed. The prediction of pedestrian risky level is realized using a data-driven method, which contains two steps, namely pedestrian trajectory prediction and risky level classification. For trajectory prediction, an LSTM network is built and trained to predict the trajectory of pedestrian in the onboard camera view. For risky level classification, to avoid manually selecting thresholds for different risky levels, a hybrid method integrating K-Means Clustering (KMC), Kernel Principal Components Analysis (KPCA) and Kernel Support Vector Machine (SVM) is applied. The pedestrian risky level is divided into four classes by KPCA-KMC, namely low risk, medium risk, high risk and super-high risk. Experimental results show the capability of PRLP model to predict the risky level of pedestrian, which could help the intelligent vehicle to assess the risk of collision with pedestrians, and provide safety warnings to both vehicle and pedestrian.