汽车工程进展
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基于预测模型的自动驾驶车辆低速巡航控制

奥尔罕·阿兰库斯、艾利夫·托伊·阿齐齐亚格达姆、卡恩·卡金

The European Union confirmed in June 2019 the “Vision Zero” goal of achieving zero fatalities and serious injuries by 2050. This can only be achieved by integrating connected and autonomous vehicles into intelligent traffic systems and sustainable mobility systems. This requires a cost-effective, fast and efficient development process for advanced connected and autonomous vehicle functions. This article explains a method for developing low-speed adaptive cruise control (ACC), one of the most important functions for autonomous vehicles. Low-speed vehicle tracking is a problem, especially for conventional vehicles with high nonlinearities in the powertrain system. As part of the university-industry collaboration project “SAE Level 3 Autonomous Bus Development”, a flexible and realistic discrete plant model including longitudinal vehicle and powertrain models was developed and a discrete low-speed ACC was designed. The plant model is designed for detailed and realistic software testing of autonomous functions interfacing with the vehicle controller. The OKAN_UTAS autocorrection multi-parameter longitudinal model is integrated. For engine modeling by axle dynamometer, a 3D map of the engine can be reproduced. The transmission characteristics are derived from road tests. To improve the reliability of the developed functions, software-in-the-loop (SIL) and model-in-the-loop (MIL) simulations were performed before road vehicle testing. Finally , C code compliant with the MISRA C standard for ACC was generated and embedded into the real-time platform.