HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles

Abstract

AbstractOver the past ten years, autonomous driving has garnered a great deal of interest from both the scientific community and business. Strong technological advancements have made automated driving more practical because human driving abilities seem limited in terms of driving experience, reaction time, and the effectiveness of real‐time decisions. The development of highly autonomous driving algorithms is inextricably tied to planning and changing a vehicle path that must be user‐acceptable, efficient, and collision‐free. Path planning for road vehicles is a difficult problem due to the high speed involved and the requirement to assure passenger safety. Here, a new path‐planning method is developed for both connected and disconnected automatic road vehicles on multilane highways. This paradigm states that the right phrases to describe the objectives of vehicle improvement, passenger comfort, prevention of vehicle‐to‐vehicle collisions and road deviations are included in the objective function. Hunger Games improved Archimedes optimization (HGE‐ARCO) is used to optimize the paths for achieving better‐planned outcomes. At the 100th penetration rate, the HGE‐ARCO scheme reached a top speed of about 99 km/h. The results shows unmistakably that the proposed HGE‐ARCO produces a time of 12.3021 s, which is less than other conventional methods.