This article proposes a novel cooperative motion planning and decision-making approach for connected and automated vehicles (CAVs) at unsignalized intersections, where a multivehicle collision-free trajectories generating problem is modeled as a constrained optimization problem. A learning-based iterative optimization (LBIO) algorithm is developed to solve the problem iteratively and obtain velocity-optimal trajectories using the historical vehicle states at previous iterations as data sets. To make the trained trajectories adapt to continuous and time-varying traffic flow, an online decision-making algorithm based on Monte Carlo tree search (MCTS) is presented to derive a time-optimal vehicle passing sequence, where a tree structure is built to efficiently express all possible cluster-dividing modes between vehicles. In addition, we propose a trajectory planning algorithm to regulate velocities of vehicles in the cooperative control area surrounding the intersection. The proposed approach is validated on the SUMO under typical intersection scenarios. Results show that our approach enables potentially conflicting vehicles to go through the intersection simultaneously without queuing and significantly improves the overall traffic efficiency.

Motion planning and control of connected and autonomous vehicles (CAVs) for improving traffic efficiency and safety in intersections still meets many challenges due to its dynamic and complex nature. In this article, an innovative collision-free and time-optimal multivehicle motion planning method for the CAVs at unsignalized intersection scenarios is proposed. We systematically analyze the regularity of intersection crossing mode and summarize the overall conflict scenario. To eliminate the vehicles potential collision, a learning-based iterative optimization (LBIO) algorithm is designed to solve the collision-free trajectories generating problem iteratively and offline. The terminal constraint set, terminal cost, and global safe constraints of the LBIO are constructed and updated from the historical data in previous iterations. The algorithm can finally converge to time-optimal trajectories for multivehicle only after several iterations. To apply the trained trajectories into the continuous intersection traffic flow, an online cluster-based motion planning (CBMP) algorithm is developed to coordinate the vehicle velocities and movements in the cooperative control area surrounding the intersection. With an LTV-MPC algorithm for the low-level control, the proposed approach is validated on the SUMO in typical intersection scenarios. The results show that the proposed method allows the potentially conflicting vehicles passing the intersection simultaneously and quickly without waiting, and significantly improves the overall traffic efficiency.

This paper proposes an iterative learning-based cooperative motion planning and decision-making approach to achieve time-optimal coordination control of connected and autonomous vehicles (CAVs) at on-ramps. In this work, a decentralized learning-based iterative optimization method (DLIO) is first developed to offline train the vehicle merging trajectories in space-time. To guarantee safety and convergence properties at each iteration, the collision-free terminal constraint set and approximated merging time cost are designed using the historical vehicle states as a dataset. For adapting the trained trajectories into dynamic traffic flow at on-ramps online, we systematically analyze the arrival time of inter-vehicle potential actions and model a decision tree to express all possible vehicle cluster passing sequences. Then, a heuristic Monte-Carlo tree search (HMCTS) algorithm with a modified searching principle is presented to derive a minimum-time passing sequence. Also, a customized two-stage velocity planning method is used to regulate the vehicle flow following the optimal sequence and trained condition. The proposed approach is verified on the SUMO and compared with three baselines under different on-ramps traffic demands. Results show that our approach enables the conflicting vehicles to merge into the mainline without queuing, rendering both robust and high efficiency multi-vehicle coordination.

Path tracking for autonomous vehicles is one of the most critical tasks in intelligent transportation systems (ITS). The ITS performance, including efficiency, safety, flexibility, and resilience, are all based on it. The two central issues for a successful path tracking are resilience and smoothness. We endeavor to adopt a neural-activation based constraint-following approach to resolve these two issues concurrently. First, an adaptive robust constraint-following control scheme is proposed. The control tracks a desired trajectory with guaranteed performance even in the presence of uncertainty. Second, a neural-activation mechanism is proposed, which generates desired trajectory effectively based on traffic pattern with sufficiently smoothness. Third, the trajectory is embedded into the control scheme to ensure that the control conforms to any changing traffic pattern while in motion. As a result, the control can rapidly adapt to the changing traffic condition with smoothness and resilience.

The behavior of connected and autonomous vehicles (CAVs) in traffic environments is very complex. Giving efficient cooperative driving strategies in traffic intersection scenarios is still a challenge, especially at roundabout intersections. In this paper, we propose a decision-making and motion planning method for roundabouts by an iterative learning-based optimization. Time-optimal safe driving strategies are given in a hybrid architecture. Based on the crossing mode modeling, a data-efficient learning-based iterative optimization (DELIO) method for offline trajectory generation is proposed. We build the optimization problem by introducing stage and terminal costs and describing the static and dynamic constraints using Support Vector Machine (SVM) method. Iterative safety constraints are constructed to store historical data for closed-loop efficient data-driven learning. The algorithm finally converges to a time-optimal collision-free trajectory only after several iterations. In order to rationally apply the optimal trajectories, a multi-vehicle cooperative decision-making and motion planning method based on adaptive Monte Carlo tree search (AMCTS) is proposed. The algorithm eliminates inter-group conflicts and quickly converges to the decision sequence of the optimal grouping. We validate our method in a typical roundabout intersection scenario. Results show that our method enables vehicles to pass through the roundabout intersection efficiently and safely, which significantly improves the traffic efficiency.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.