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首页> 外文期刊>Accident Analysis and Prevention >Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network
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Prediction of pedestrian-vehicle conflicts at signalized intersections based on long short-term memory neural network

机译：基于长短期记忆神经网络的信号交叉口的行人车辆冲突预测

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摘要

Pedestrian protection is an important component of road safety. Intersections are dangerous locations for pedestrians with mixed traffic. This paper aims to predict potential traffic conflicts between pedestrians and vehicles at signalized intersections. Using detection and tracking techniques in computer vision, pedestrians' and vehicles' features are extracted from video data. An LSTM (Long Short-term Memory) neural network is proposed to predict the pedestrian-vehicle conflicts 2 s ahead. The established model reaches an accuracy of 88.5 % at one signalized intersection. It is further tested at a new intersection, reaching the accuracy of 84.9 %, while the new data merely takes up 30 % of the training data set. This indicates that the proposed model is promising to be implemented at different locations. Moreover, the proposed model can also be applied to develop collision warning systems under the Connected Vehicles' environment.
机译：行人保护是道路安全的重要组成部分。交叉路口是具有混合交通的行人的危险地点。本文旨在预测信号交叉口的行人和车辆之间的潜在交通冲突。使用计算机视觉中的检测和跟踪技术，从视频数据中提取行人“和车辆的特征。提出了LSTM（长期内存）神经网络，以预测前方的行人冲突2。既定模型在一个信号交叉点达到88.5％的准确性。它在新的交叉路口进一步测试，达到84.9％的准确性，而新数据仅占培训数据集的30％。这表明该拟议的模型是有希望在不同地点实施。此外，所提出的模型也可以应用于在连接的车辆环境下开发碰撞警告系统。

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