## 1 Introduction  
Platoon refers to autonomous vehicles that travel compactly and steadily with a short inter-vehicle distance. Because platooning technology enables the vehicles to maintain high speeds with less aerodynamic drag, the improvement is noticeable, especially for heavy-duty trucks [1, 2]. Truck platooning, which is mostly employed for long-distance transportation on highways, is considered one of the most promising scenarios for the early commercial application of vehicle automation on open roads. Therefore, research on truck platooning technology has recently become a central focus of scholars in the field of automated driving. Truck platooning can avoid the slow response times and operational errors associated with human drivers, thus improving the safety of highway traffic. Because trucks in platooning systems can be driven with minimal vehicle spacing, the approach may help improve highway capacity, reduce fuel consumption and the operation costs of trucks, and improve the road environment and air quality [3, 4, 5, 6].  
With the aid of Vehicle-to-Vehicle communication [7] and Cooperative Adaptive Cruise Control [8], truck platooning systems and pioneering projects such as SARTRE [9], GCDC [10], Energy ITS [11] and ENSEMBLE [12] have shown pronounced benefits, including higher safety, better energy consumption, and greater comfort. Truck platooning studies generally establish a hierarchical control method with two levels of platooning: platooning control and platooning coordination [13]. The optimal objectives are the combination of vehicle speed, fuel economy, and instantaneous stability [14, 15, 16, 17]. The management of truck platoons can not be isolated from the actual environment and traffic dynamics [18]; thus, vehicle-infrastructure cooperation or global traffic optimization must necessarily be involved. Previous studies have combined truck platooning with road traffic flow states, analyzed the optimal control of truck platooning, improved bottleneck congestion as the optimization goal [19; 20; 21], and studied the formation, maintenance or separation strategy of truck platooning in special conditions of mixed traffic flow [22; 23].  
From the perspective of the traffic authority, platooning systems shall ensure efficient traffic operations at the macro level. However, there has been an insufficient amount of research conducted on the combination of truck platooning and truck lane management strategies for multilane highways. Existing truck lane management studies on highways largely examined how to set up lanes for autonomously driven trucks through a simulation platform to ensure traffic safety and improve traffic efficiency [24; 25; 26]. In general, research pertaining to truck platooning oriented toward vehicle-infrastructure cooperation and traffic flow optimization, as well as truck-lane management strategies on multilane highways, needs to be further strengthened.  
This study established a multi-objective optimization truck platooning method for optimal speed control that considers global transportation optimization at the bottleneck, as well as individual energy consumption, for connected automated vehicle highways. In the process of this study, several truck lane management strategies were examined. Accordingly, this paper analyzes their influence on highway traffic and suggests additional approaches for highway management. The contributions of this study include: (1) A speed control model for truck platooning based on a multi-objective optimization, with the combination of macro-traffic flow optimization and energy consumption of automated trucks under the vehicle-infrastructure collaborative environment; (2) Targeting the management requirements of truck platooning technology in multilane highways under the CVIS environment, a set of truck lane management strategies for traffic efficiency and safety in the weaving section was proposed, thus providing decision-making support for highway lane management and traffic operations.