The platooning of automated vehicles has potential to significantly benefit road traffic, while its robust performance is less investigated especially considering increasing complexity of interaction topologies. This study presents a decoupled H ∞ control method for automated vehicular platoon to comprehensively compromise multiple performances. The platoon control system is first decomposed into an uncertain part and a diagonal nominal system through the linear transformation, which is motivated by the eigenvalue decomposition of information topology. Based on this almost decoupled system, a distributed H ∞ controller is presented, which can balance the performances of robustness and disturbance attenuation ability. Moreover, a numerical method is given to solve and optimise this controller by using linear matrix inequality approach. Several comparative hardware-in-loop tests of different communication topologies and controllers have been carried out to demonstrate the effectiveness of this method.

In this paper, a methodology of automatic generation of test scenarios for intelligent driving systems is proposed, which is based on the combination of the test matrix (TM) and combinatorial testing (CT) methods together. With a hierarchical model of influence factors, an evaluation index for scenario complexity is designed. Then an improved CT algorithm is proposed to make a balance between test efficiency, condition coverage, and scenario complexity. This method can ensure the required combinational coverage and at the same time increase the overall complexity of generated scenarios, which is not considered by CT. Furthermore, the way to find the best compromise between efficiency and complexity and the bound of scenario number has been analyzed theoretically. To validate the effectiveness, it has been applied in the hardware-in-the-loop (HIL) test of a lane departure warning system (LDW). The results show that the proposed method can ensure required coverage with a significantly improved scenario complexity, and the generated test scenario can find system defects more efficiently.

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