A Convex Optimal Control Framework for Autonomous Vehicle Intersection Crossing

TL;DR

This paper presents a convex optimization framework for autonomous vehicle intersection crossing that optimizes crossing order and speed to minimize energy and time, ensuring safety and efficiency.

Contribution

It introduces a hierarchical centralized control scheme with convexified optimization problems for safe, energy-efficient intersection management of connected autonomous vehicles.

Findings

Convex relaxation provides tight bounds and guarantees a global optimum.

The approach outperforms FIFO policy in simulation case studies.

Trade-offs between travel time and energy consumption are significant and can be optimized.

Abstract

Cooperative vehicle management emerges as a promising solution to improve road traffic safety and efficiency. This paper addresses the speed planning problem for connected and autonomous vehicles (CAVs) at an unsignalized intersection with consideration of turning maneuvers. The problem is approached by a hierarchical centralized coordination scheme that successively optimizes the crossing order and velocity trajectories of a group of vehicles so as to minimize their total energy consumption and travel time required to pass the intersection. For an accurate estimate of the energy consumption of each CAV, the vehicle modeling framework in this paper captures 1) friction losses that affect longitudinal vehicle dynamics, and 2) the powertrain of each CAV in line with a battery-electric architecture. It is shown that the underlying optimization problem subject to safety constraints for powertrain operation, cornering and collision avoidance, after convexification and relaxation in some aspects can be formulated as two second-order cone programs, which ensures a rapid solution search and a unique global optimum. Simulation case studies are provided showing the tightness of the convex relaxation bounds, the overall effectiveness of the proposed approach, and its advantages over a benchmark solution invoking the widely used first-in-first-out policy. The investigation of Pareto optimal solutions for the two objectives (travel time and energy consumption) highlights the importance of optimizing their trade-off, as small compromises in travel time could produce significant energy savings.