With the introduction of autonomous vehicles, interest in platooning of Heavy Goods Vehicle (HGV) is gradually on the rise. Platooning of HGVs has several benefits such as an increase in fuel efficiency, reduction of congestion on roads, and lower costs incurred in operating a fleet. Therefore, several researchers are trying to address this problem by developing vehicle platooning algorithms that will allow HGVs to drive on highways in a tight platoon formation. This article proposes a Proportional Integral Derivative (PID) controller based on the combination of Constant Distance (CD) and Constant Headway Time (CHT) policies to operate an HGV platoon in the Cooperative Adaptive Cruise Control (CACC) mode. In addition to CACC, the controller is tested and verified to carry out splitting and merging maneuvers. An Appeal, Reply, and Implementation (ARI) protocol has been proposed as the communication paradigm for the execution of splitting and merging maneuvers. The design of the protocols is carried out to make it easier for implementing any complex platoon formation or dissolution. Furthermore, the controller performance is analyzed in the presence of Vehicle-to-Vehicle (V2V) communication constraints among the platoon vehicles. Results on Packet Delivery Ratio (PDR) among platoon vehicles have been obtained for traffic scenarios with and without the influence of the surrounding traffic on V2V communications. The proposed controller is validated with the help of an integrated simulation environment comprising of MATLAB, VISSIM, and the Network Simulator (NS3), the controller performance is analyzed for both urban arterial and highway traffic scenarios. The simulator capabilities are demonstrated by testing platooning under different traffic conditions. The contribution of this article is principally toward the design of a platoon controller that allows a long HGV platoon to execute safety-critical maneuvers such as split and merge under communication constraints. The results show that the controller can maintain the desired constant distance and time gap. Finally, the error minimization parameters such as the Integral Absolute Error (IAE), the Integral Square Error (ISE), and the Mean Square Error (MSE) are compared with an existing CACC algorithm. It is observed that the worst-case MSE in speed for the proposed controller is reduced to 246.996 from 353.91. Similarly, the worst-case MSE in intervehicular distance is reduced to 2.02 from 3.513.
