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). Numerical examples show that the proposed strategy leads to good traffic performance in real-time.

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Tree Carlo Monte

Huile Xu, Christos G. Cassandras, Li Li, Yi Zhang, Shuo Feng,.A bi-level cooperative driving strategy allowing lane changes. 120 (),102773.

Huile Xu, et al."A bi-level cooperative driving strategy allowing lane changes" 120

Huile Xu, Christos G. Cassandras, Li Li, Yi Zhang, Shuo Feng,"A bi-level cooperative driving strategy allowing lane changes"