A Bi-Level Cooperative Driving Strategy Allowing Lane Changes

TL;DR

This paper introduces a bi-level cooperative driving strategy for connected and automated vehicles that enables safe lane changes at conflict areas, using Monte Carlo Tree Search for real-time planning.

Contribution

It proposes a novel bi-level planning framework that incorporates lane changes into cooperative driving, addressing safety and efficiency in conflict zones.

Findings

Enables safe lane changes in cooperative driving scenarios.

Achieves near-optimal solutions with short planning times.

Improves traffic performance in numerical simulations.

Abstract

This paper studies the cooperative driving of connected and automated vehicles (CAVs) at conflict areas (e.g., non-signalized intersections and ramping regions). Due to safety concerns, most existing studies prohibit lane change since this may cause lateral collisions when coordination is not appropriately performed. However, in many traffic scenarios (e.g., work zones), vehicles must change lanes. To solve this problem, we categorize the potential collision into two kinds and thus establish a bi-level planning problem. The right-of-way of vehicles for the critical conflict zone is considered in the upper-level, and the right-of-way of vehicles during lane changes is then resolved in the lower-level. The solutions of the upper-level problem are represented in tree space, and a near-optimal solution is searched for by combining Monte Carlo Tree Search (MCTS) with some heuristic rules within a very short planning time. The proposed strategy is suitable for not only the shortest delay objective but also other objectives (e.g., energy-saving and passenger comfort). Numerical examples show that the proposed strategy leads to good traffic performance in real-time.