A comprehensive high-level automated driving assistance system with integrated multi-functionality

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Smart Cities Pub Date : 2024-02-26 DOI:10.1049/smc2.12076
Aijing Kong, Peng Hang, Yu Tang, Xian Wu, Xinbo Chen
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Abstract

Advanced Driver Assistance Systems (ADAS) have gained substantial attention in recent years. However, the integration mechanism of multiple functions within ADAS remains unexplored, and the full potential of its functionality remains underutilised. This paper presents a novel multi-functional integrated High-level Automated Driving Assistance System that combines the Cruise Control (CC), Adaptive Cruise Control (ACC), Automated Emergency Brake (AEB), and Automated Lane Change (ALC) functions. The presented system utilises a hierarchical framework. The extension multi-mode switch strategy is established as the superior module and the Event-Triggered Model Predictive Controller (ETMPC) is designed as the inferior controller. The CC, ACC, and ALC functions are effectively utilised to enhance traffic efficiency, while the AEB function ensures driving safety. To address the time constraints of conventional Model Predictive Control, an event-trigger mechanism is proposed to reduce computational load. Simulations are conducted using the CarSim and Matlab platforms. The study results demonstrate significant improvements in both safety and traffic efficiency compared to conventional ADAS strategies. Furthermore, the proposed ETMPC method significantly reduces the frequency of solving Optimisation Problems and decreases online computation costs.

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集多功能于一体的综合性高级自动驾驶辅助系统
先进驾驶辅助系统(ADAS)近年来备受关注。然而,ADAS 中多种功能的集成机制仍未得到探索,其功能潜力仍未得到充分发挥。本文介绍了一种新型的多功能集成高级别自动驾驶辅助系统,该系统结合了巡航控制(CC)、自适应巡航控制(ACC)、自动紧急制动(AEB)和自动变道(ALC)功能。该系统采用分层框架。扩展多模式切换策略被确立为上级模块,事件触发模型预测控制器(ETMPC)被设计为下级控制器。有效利用 CC、ACC 和 ALC 功能提高交通效率,同时利用 AEB 功能确保行车安全。针对传统模型预测控制的时间限制,提出了一种事件触发机制,以减少计算负荷。研究使用 CarSim 和 Matlab 平台进行了仿真。研究结果表明,与传统的 ADAS 策略相比,ETMPC 在安全性和交通效率方面都有显著提高。此外,所提出的 ETMPC 方法大大降低了解决优化问题的频率,降低了在线计算成本。
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来源期刊
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
期刊最新文献
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