Longitudinal Deep Truck: Deep learning and deep reinforcement learning for modeling and control of longitudinal dynamics of heavy duty trucks

Abstract

Heavy duty truck mechanical configuration is often tailor designed and built for specific truck mission requirements. This renders the precise derivation of analytical dynamical models and controls for these trucks from first principles challenging, tedious, and often requires several theoretical and applied areas of expertise to carry through. This article investigates deep learning and deep reinforcement learning as truck-configuration-agnostic longitudinal modeling and control approaches for heavy duty trucks. The article outlines a process to develop and validate such models and controllers and highlights relevant practical considerations. The process is applied to simulation and real-full size trucks for validation and experimental performance evaluation. The results presented demonstrate applicability of this approach to trucks of multiple configurations; models generated were accurate for control development purposes both in simulation and the field.

Tasks

Reproductions