Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations require junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e., fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV’s and the platoon’s predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with a Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method.

联网和自动驾驶汽车 (CAV) 编队可以提高交通和燃油效率。然而，可扩展的编队作战需要交汇点级协调，这一点尚未得到很好的研究。在本文中，我们研究了高速公路路口车辆排队的协调。我们考虑一种设置，其中 CAV 根据一般更新过程随机到达高速公路路口。当 CAV 接近路口时，系统操作员会根据 CAV 和排的位置和速度来确定 CAV 是否​​会并入前方的排。我们制定了一个马尔可夫决策过程，以在无限的时间范围内最小化折现累积旅行成本，即燃料消耗加上旅行延迟。我们表明最优策略是基于阈值的：当且仅当 CAV 与排到达路口的预测时间之间的差异小于恒定阈值时，CAV 才会与排合并。我们还提出了两种现成的算法来推导出最优策略。与经典值迭代算法的比较表明，我们的方法明确地结合了最优策略的特征，在计算方面显着提高了效率。重要的是，我们表明可以通过求解积分方程组来获得泊松到达下的最优策略。我们还使用真实交通数据通过实时策略 (RTS) 在模拟中验证了我们的结果。仿真结果表明，与传统方法相比，所提出的方法具有更好的性能。