Truck Driving Assistance System

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) Pub Date : 2021-11-24 DOI:10.1109/SNPD51163.2021.9704970

Chi-Chun Chen, Shang-Lin Tien, Yanhui Lin, Chung-Chen Teng, Meng-Hua Yen

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Abstract

Eco-driving is an effective and immediate environmental protection and energy saving method. This research assists occupational driving license training to achieve eco-driving at two parts: 1. Combine g-sensor with on board diagnostics (OBD-II) and add parameters to improve the data analysis. 2. Through two kinds of neural network models, predict fuel consumption to analyze driving style, and provide reports to display evaluation and behavior suggestions. The experimental configuration designed in this research includes user interface, OBD-II system, neural network model, and is applied to public institutions to provide assistance. The results of this study show that the accuracy of predicting fuel consumption exceeds 97%, which verifies the practicability of the system. The system will also help extend other related applications, such as achieving a driving behavior model that compares energy saving and safety.

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卡车驾驶辅助系统

生态驾驶是一种有效、快捷的环保节能方式。本研究从两个方面协助职业驾驶执照培训实现生态驾驶。将g传感器与车载诊断(OBD-II)相结合，并添加参数以改进数据分析。2. 通过两种神经网络模型，预测油耗，分析驾驶风格，并提供报告显示评价和行为建议。本研究设计的实验配置包括用户界面、OBD-II系统、神经网络模型，并应用于公共机构提供辅助。研究结果表明，油耗预测准确率超过97%，验证了该系统的实用性。该系统还将有助于扩展其他相关应用，例如实现比较节能和安全的驾驶行为模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

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