Predictive Model Based Low-Speed Adaptive Cruise Control for Autonomous Vehicles

Abstract

European Union confirmed the “Vision Zero” objective in June 2019, as to achieve zero deaths and serious injuries by 2050. This can only be attained through connected and autonomous vehicles integrated into intelligent transport systems and a sustainable mobility system. This requires a cost-effective, fast,and efficient development process for advanced connected and autonomous vehicle functions. In this article, a methodology to develop low-speed Adaptive Cruise Control (ACC), which is one of the most important functions of an autonomous vehicle, is explained. Vehicle tracking at slow speeds is a problem especially for conventional vehicles with high levels of nonlinearities in the powertrain system. As a part of a university-industry collaboration project “SAE level 3 autonomous bus development”, a flexible and realistic discrete plant model including longitudinal vehicle and powertrain model has been developed and discrete low-speed ACC is designed. The plant model aims to perform detailed and realistic software tests of autonomous features, which interfaces with the vehicle controllers. OKAN_UTAS autocorrectedmulti-parameter longitudinal model is integrated. For engine modeling via shaft dynamometer the 3D map of the engine is reproduced. The transmission characteristics were prepared through the road tests. To increase the reliability of the developed functions, Software in the Loop (SIL) and Model in the Loop (MIL) simulations were conducted before the on-road vehicle tests. Finally, C code with the MISRA C standard of ACC is generated and embedded into a real-time platform. The plant model, ACC design, and Model in the Loop test results are presented.