Energy-efficient predictive control for connected, automated driving under localization uncertainty

TL;DR

This paper introduces a data-driven Model Predictive Control approach for energy-efficient urban driving of connected automated vehicles, optimizing energy use while navigating traffic lights and front vehicles under localization uncertainty.

Contribution

It develops a novel MPC with learned terminal cost and constraints from data, improving energy efficiency in urban driving scenarios for connected automated vehicles.

Findings

19% energy efficiency improvement over conventional methods

Validated through simulations and vehicle-in-the-loop experiments

Effective handling of localization uncertainty and traffic constraints

Abstract

This paper presents a data-driven Model Predictive Control (MPC) for energy-efficient urban road driving for connected, automated vehicles. The proposed MPC aims to minimize total energy consumption by controlling the vehicle's longitudinal motion on roads with traffic lights and front vehicles. Its terminal cost function and terminal constraints are learned from data, which consists of the closed-loop state and input trajectories. The terminal cost function represents the remaining energy-to-spend starting from a given terminal state. The terminal constraints are designed to ensure that the controlled vehicle timely crosses the upcoming traffic light, adheres to traffic laws, and accounts for the front vehicles. We validate the effectiveness of our method through both simulations and vehicle-in-the-loop experiments, demonstrating 19% improvement in average energy efficiency compared to conventional approaches that involve solving a long-horizon optimal control problem for speed planning and employing a separate controller for speed tracking.