Task-driven Modular Co-design of Vehicle Control Systems

TL;DR

This paper presents a formalized, modular co-design approach for vehicle control systems that jointly optimizes control algorithms and platform hardware/software, enabling efficient Pareto optimal solutions in urban driving scenarios.

Contribution

It introduces a monotone co-design framework for vehicle control systems, integrating control synthesis with platform design for the first time.

Findings

Efficient computation of Pareto optimal solutions for vehicle control co-design.

Formalization of control schemes as monotone feasibility relations.

Application to urban driving scenarios demonstrating practical effectiveness.

Abstract

When designing autonomous systems, we need to consider multiple trade-offs at various abstraction levels, and the choices of single (hardware and software) components need to be studied jointly. In this work we consider the problem of designing the control algorithm as well as the platform on which it is executed. In particular, we focus on vehicle control systems, and formalize state-of-the-art control schemes as monotone feasibility relations. We then show how, leveraging a monotone theory of co-design, we can study the embedding of control synthesis problems into the task-driven co-design problem of a robotic platform. The properties of the proposed approach are illustrated by considering urban driving scenarios. We show how, given a particular task, we can efficiently compute Pareto optimal design solutions.