Abstract
In the context of automated highway systems (AHS), this work proposes an approach that enables a vehicle to autonomously join a platoon with optimized trajectory in the presence of dynamical traffic obstacles. A notable aspect is the use of Model Predictive Control (MPC) optimization of the planned path, in conjunction with a variant of the Rapidly-exploring Random Trees (RRT*) algorithm for the purpose of platoon formation. This combination efficiently explores the space of possible trajectories, returning trajectories that are smoothened out with respect to the dynamic constraints of the vehicle, while at the same time allowing for real-time implementation. The implementation we propose takes into consideration both localization and mapping through relevant sensors and V2V communication. The complete algorithm is tested over various nominal and worst-case scenarios, qualifying the merits of the proposed methodology.
Highlights
Energy consumption related to transport, combined with air quality issues, continues to attract the attention of scientists
We describe the implementation of autonomous path planning for platoon formation on highways, via an algorithmic pipeline where “slave” vehicles are guided by a “lead” vehicle through relevant sensors and V2V communication, validated in a virtual vehicle environment
All the candidates are ranked based on their Rapid-Exploring Random Trees (RRT)* cost which is the length of the raw RRT* path
Summary
Energy consumption related to transport, combined with air quality issues, continues to attract the attention of scientists. Several driving scenarios can be compatible with eco-driving criteria One such scenario is car following where a minimum safety distance has to be kept with respect to the leading vehicle. This can be in the form of a constant distancing between two vehicles in a row, keeping a minimum safe distance [1,2], constant time headway policy where the desired distance is velocity dependent [3,4] or in the form of nonlinear spacing policy [5,6,7]. Highways represent favorably constrained environments for autonomous vehicles and in this context platooning forms a roadmap towards automated highway systems (AHS) [1,12], whereby a dedicated “lead” vehicle, fully equipped with the required sensors, can guide other vehicles (minimally equipped) into a platoon and maintain it
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