Parallel vehicles based on the ACP theory: Safe trips via self-driving

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI:10.1109/IVS.2017.7995693

Shuangshuang Han, Feiyue Wang, Yingchun Wang, Dongpu Cao, Li Li

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引用次数: 8

Abstract

With the development of intelligent technologies, self-driving vehicles are considered as a promising solution against accident, traffic congestion and pollution problems. Intelligent vehicle techniques have been the research focus all over the world. However, full self-driving vehicles are still far away from its realization and extensive application due to safety requirements and cost considerations. As a novel breakthrough, PArallel VEhicles (PAVE) incorporate the ACP theory, which facilitates real-time interaction and optimization of the actual self-driving vehicles and the artificial ones. As a result, PAVE can maintain intelligent control of the actual self-driving vehicles and achieve the global optimization via software-defined self-driving vehicles, intelligent infrastructure construction, and parallel control center. Besides, PAVE can effectively reduce the cost of high-precision equipments on the actual self-driving vehicles via remote processing and intelligent road(side) infrastructure, and also achieve improved safety and reliability via remote control, guidance and planning.

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基于ACP理论的平行车辆:自动驾驶安全行驶

随着智能技术的发展，自动驾驶汽车被认为是解决事故、交通拥堵和污染问题的有希望的解决方案。智能汽车技术一直是世界各国研究的热点。然而，由于安全要求和成本的考虑，全自动驾驶汽车距离其实现和广泛应用还很遥远。PArallel VEhicles (PAVE)作为一项新的突破，融入了ACP理论，实现了实际自动驾驶车辆与人工自动驾驶车辆的实时交互和优化。因此，PAVE可以通过软件定义的自动驾驶车辆、智能基础设施建设、并行控制中心，保持对实际自动驾驶车辆的智能控制，实现全局优化。此外，PAVE可以通过远程处理和智能路(边)基础设施有效降低实际自动驾驶车辆上高精度设备的成本，也可以通过远程控制、引导和规划来提高安全性和可靠性。

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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2017 IEEE Intelligent Vehicles Symposium (IV)

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