Freeway-diverging areas are prone to low traffic efficiency, congestion, and frequent accidents. Because of the fluctuation of the surrounding traffic flow distribution, the individual decision-making of vehicles in diverging areas is typically unable to plan a departure trajectory that balances safety and efficiency well. Consequently, it is critical that vehicles in freeway-diverging regions develop a lane-changing driving strategy that strives to improve both the safety and efficiency of divergence areas. For CAV leaving the diverging area, this study suggested a full-time horizon optimum solution. Since it is a dynamic strategy, an MPC system based on rolling time horizon optimization was constructed as the primary algorithm of the strategy. A simulation experiment was created to verify the viability of the proposed methodology based on a mixed-flow environment. The results show that, in comparison with the feasible strategies exiting to off-ramp, the proposed strategy can take over 60% reduction in lost time traveling through a diverging area under the premise of safety and comfort without playing a negative impact on the surrounding traffic flow. Thus, the MPC system designed for the subject vehicle is capable of performing an optimal driving strategy in diverging areas within the full-time and space horizon.

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following–merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

Currently, the transportation industry has deficiencies in evaluating the effect of freight automated driving demonstration applications, Demonstration application programs focus only on whether it is there or not, there is no “good” or “bad” evaluation, and cannot provide positive feedback for the improvement of autonomous driving technology and industry application. This paper applies the AHP-fuzzy comprehensive evaluation method to construct an evaluation model for the effect evaluation of freight automated driving demonstration applications. Firstly it establishes an evaluation system, which contains 6 first-level indicators and 20 second-level indicators. Then it applies the expert research method and AHP method to determine the weights of the indicators, and builds an evaluation model using the fuzzy evaluation method. Finally, “Tianjin Port to Majuqiao Logistics Park Highway Automated Freight Driving Pilot” is selected as a case study for engineering evaluation. The evaluation result indicates that the evaluation grade of this pilot is between “better” and “good”, which verifies the feasibility of the method and provides a scientific basis for the effect evaluation of freight automated driving demonstration application.