This survey paper explores the emergent domain of electric vertical takeoff and landing vehicles (eVTOLs), emphasizing the critical role of autonomous navigation capabilities essential for their effective integration and operation in complex urban environments. Pioneering in this review is the introduction of a novel six-level autonomy concept for eVTOLs, categorizing them based on their degree of intelligence. The paper offers a comprehensive review of state-of-the-art developments that together fortify the autonomous functionality of eVTOLs, with a special focus on enhanced perception, intelligent planning, and advanced control. Perception technologies empower eVTOLs with the environmental awareness crucial for navigating the intricate urban airspace, while advanced planning algorithms navigate their paths through densely populated skies, ensuring optimal routing and safety. Control strategies are developed for their ability to endow these eVTOLs with the stability and agility needed to execute complex flight dynamics. The synthesis of these elements forms the backbone of autonomous navigation for eVTOLs, outlining a clear direction for future eVTOL research. This comprehensive survey can serve as a vital resource for enhancing the autonomy of eVTOLs, steering them towards a future where they integrate effortlessly into intelligent urban transportation systems.

This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed to address this problem. At triggering instants, each agent solves a local min–max optimization problem based on local system states and predicted states of neighbors, determines its next triggering instant, and broadcasts its predicted state trajectory to the neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic DMPC algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcasted predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of the asynchronous communication and time-varying delays. The proposed algorithm’s recursive feasibility and MAS’s closed-loop stability at triggering instants are proven. Finally, numerical simulations are conducted to verify the theoretical results.

In this paper, an asynchronous stochastic self-triggered distributed MPC (DMPC) control scheme is proposed for vehicular platoon systems under coupled state constraints and additive stochastic disturbance. In considered platoon systems, each vehicle broadcasts its predicted state as beacon information to its neighbouring vehicles through the vehicular ad-hoc network (VANET). To reduce the communication burden in the VANET, each vehicle proactively determines the next sampling time instant by solving the stochastic self-triggered DMPC problem at the sampling time instant. The self-triggered problem is formulated by utilizing local vehicle states and asynchronous beacon information from its neighbours. Consequently, the proposed scheme reduces the communication load dramatically in the VANET while maintaining a satisfactory control performance compared to periodic time-triggered stochastic DMPC. To handle the state coupling between vehicles, e.g., for collision avoidance or communication connectivity purpose, probabilistic coupled state constraints are incorporated into the DMPC problem. Based on the information on stochastic disturbance, the probabilistic coupled state constraints are transformed into deterministic forms using the stochastic tube-based method. Theoretical analysis has shown that closed-loop platooning is quadratically stable at triggering time instants. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reduction.

Accurate information on tire road friction coefficient (TRFC) is essential to autonomous driving systems. In this paper, a fault-tolerant estimation scheme is proposed to estimate TRFC in the case of missing measurements. First, a fault-tolerant unscented Kalman filter (FTUKF) is developed for estimating longitudinal and lateral tire forces in the condition of sensor signal loss. Then, longitudinal and lateral TRFCs are estimated separately with FTUKF based on tire forces information. Next, an event-driven multi-model fusion method based on the degree of data loss is designed to perform a weighted fusion of longitudinal and lateral TRFCs to further improve the estimation accuracy. Experiments with different working conditions are performed to demonstrate the validity of the fault-tolerant estimation framework. The results illustrate that the designed approach has higher estimation accuracy and strong adaptability under various roads.