  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.
