In emerging mixed traffic environments, connected and autonomous vehicles (CAVs) must interact with surrounding human-driven vehicles (HDVs). This study introduces multisource human-in-the-loop mixed cloud control testbed (MSH-MCCT), a novel CAV testbed that captures complex interactions between various CAVs and HDVs. Utilizing the mixed digital twin concept, which combines mixed reality with digital twins, MSH-MCCT integrates physical, virtual, and mixed platforms, along with multisource control inputs. Bridged by the mixed platform, MSH-MCCT allows human drivers and CAV algorithms to operate both physical and virtual vehicles within multiple fields of view. In particular, this testbed facilitates the coexistence and real-time interaction of physical and virtual CAVs and HDVs, significantly enhancing the experimental flexibility and scalability. Experiments on vehicle platooning in mixed traffic showcase the potential of MSH-MCCT to conduct CAV testing with multisource real human drivers in the loop through driving simulators of diverse fidelity.

Formation control of Connected and Autonomous Vehicles (CAVs) has shown significant potential for improving traffic safety and efficiency in multi-lane traffic. However, previous work has primarily focused on spatial coordination while neglecting temporal optimization, which significantly limits their cooperation capability and practical applicability in real-world traffic. In this study, we propose a Spatio-Temporal Formation Control (STFC) method that integrates centralized formation generation with distributed trajectory planning. Precisely, we propose a graph-based formation maintenance representation, and show that the interlaced geometric structure is optimal for multi-lane formation. Then, we develop a distributed spatio-temporal joint formation trajectory planning method that simultaneously optimizes spatial positions and temporal duration, with consideration of multiple objectives such as formation maintenance and obstacle avoidance. Further, we design a polynomial vehicle-to-target assignment algorithm that inherently resolves conflicts. Simulation experiments demonstrate the superiority of our method over baselines in terms of formation maintenance and transition, achieving a 53% and 58% reduction in transition time and distance, respectively.