Developing a New Driver Assistance System for Overtaking on Two-Lane Roads using Predictive Models

S. A. Fadhil, A. Al-Bayatti
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引用次数: 1

Abstract

The complexity of an overtaking maneuver on two-lane roads merits a thorough method for developing an assistance system to prevent accidents, thus reducing the number of fatalities and the associated economic costs. This research aims to introduce a new Driver Overtaking Assistance System (DOAS). This system is based on the proactive prediction of the possibility of overtaking any preceding vehicle(s) both accurately and safely. To provide a comprehensive system, different factors related to the driver, the vehicle, the road, and the environment which have an impact on the maneuver have been taken into consideration. In addition to considering the main overtaking strategies including accelerative, flying, piggybacking, and the 2+. The proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity through Hello beacon messages and a set of sensors that are used as part of the reasoning process of the context-aware architecture to safely initiate the overtaking maneuver. A classification model was implemented for both the Artificial Neural Network (ANN) and Support Vector Machine (SVM) learning algorithms. A vehicle driving simulator STISIM Drive® was used to conduct driving experiments for 100 participants of different ages, gender, and levels of mental awareness. The results obtained from the DOAS show high accuracy in aiding a safe overtaking maneuver. The classification model shows promising results in the predictions, through perfect accuracy and a very low level of outcome errors.
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基于预测模型的双车道超车辅助系统研究
双车道道路上超车操作的复杂性值得采用一种彻底的方法来开发辅助系统以防止事故发生,从而减少死亡人数和相关的经济成本。本研究旨在介绍一种新型的驾驶员超车辅助系统(DOAS)。该系统基于对准确安全地超越任何前方车辆的可能性的主动预测。为了提供一个全面的系统,考虑了与驾驶员、车辆、道路和环境相关的不同因素,这些因素对机动有影响。除了考虑主要的超车策略,包括加速、飞行、背负和2+。所提出的系统是一个基于车辆的安全系统,基于通过Hello信标消息和一组传感器从驾驶附近收集上下文信息,这些传感器被用作上下文感知架构的推理过程的一部分,以安全地启动超车动作。实现了人工神经网络(ANN)和支持向量机(SVM)学习算法的分类模型。车辆驾驶模拟器STISIM Drive®用于对100名不同年龄、性别和心理意识水平的参与者进行驾驶实验。从DOAS获得的结果表明,在协助安全超车机动方面具有高精度。该分类模型通过完美的准确性和极低的结果误差,在预测中显示出了有希望的结果。
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来源期刊
Periodica Polytechnica Transportation Engineering
Periodica Polytechnica Transportation Engineering Engineering-Automotive Engineering
CiteScore
2.60
自引率
0.00%
发文量
47
期刊介绍: Periodica Polytechnica is a publisher of the Budapest University of Technology and Economics. It publishes seven international journals (Architecture, Chemical Engineering, Civil Engineering, Electrical Engineering, Mechanical Engineering, Social and Management Sciences, Transportation Engineering). The journals have free electronic versions.
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