# A Bi-Level Cooperative Driving Strategy Allowing Lane Changes

arXiv: https://arxiv.org/abs/1912.11495 · HTML: https://mlsanity.com/paper/1912.11495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles](https://mlsanity.com/paper/2409.13783/) (2024)
- [Coordinating CAV Swarms at Intersections with a Deep Learning Model](https://mlsanity.com/paper/2211.05297/) (2022)
- [Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles](https://mlsanity.com/paper/2412.04341/) (2024)

## Abstract (arXiv)

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.