This paper focuses on the multi-vehicle motion planning (MVMP) problem for cooperative lane changes of connected and automated vehicles (CAVs). The predominant decentralized MVMP methods can hardly explore and utilize the cooperation capability of a multi-vehicle team, thus they usually lead to low-quality solutions. This paper proposes a two-stage MVMP framework to find high-quality online solutions. Concretely, at stage 1, the CAV platoon transfers from its original formation to a sufficiently sparse formation; at stage 2, all the CAVs simultaneously change lanes with collision avoidance implicitly ensured. The CAVs only involve longitudinal rather than lateral motions at stage 1, thus the collision-avoidance constraints can be easily handled. Since stage 2 begins with a sparse formation, the implicitly ensured collision avoidance can be completely omitted then. Through this, the proposed method avoids directly handling the challenging collision avoidance conditions, thereby being able to compute fast. As the vehicles run cooperatively and simultaneously at either stage, the obtained solutions are near-optimal. The completeness, effectiveness, and quality of the proposed two-stage MVMP method are validated through theoretical analysis and comparative simulations.

Balancing Computation Speed and Quality: A Decentralized Motion Planning Method for Cooperative Lane Changes of Connected and Automated Vehicles

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.

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Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

In this paper, we propose a cooperative motion planning method for a group of connected and automated vehicles (CAVs) crossing a lane-free intersection without using explicit traffic signaling. This multi-vehicle motion planning task is formulated as a centralized optimal control problem. However, the solution to this optimal control problem is numerically intractable due to the high dimensionality of the collision-avoidance constraints and the nonlinearity of the vehicle kinematics. A two-stage strategy is proposed for generating online solutions: at Stage 1, the CAVs are requested to reach a standard formation before entering the intersection; at Stage 2, the vehicles cross the intersection. As the motion planning sub-problem at Stage 2 begins with a standard configuration, the optimal solution to this standard sub-problem can be computed offline in advance and applied online directly. On the other hand, the formation reconfiguration sub-problem at Stage 1 is easy to solve online. Through dividing the entire dynamic process into two periods, the difficulties in the original optimal control problem are significantly reduced so that the real-time performance is achieved.

Near-Optimal Online Motion Planning of Connected and Automated Vehicles at a Signal-Free and Lane-Free Intersection

This work formulates the multi-vehicle lane change motion planning task as a centralized optimal control problem, which is beneficial in being generic and complete. However, a direct solution to this optimal control problem is numerically intractable due to the dimensionality of the collision-avoidance constraints and nonlinearity of the vehicle kinematics. A progressively constrained dynamic optimization (PCDO) method is proposed to facilitate the numerical solving process of this complicated problem. PCDO guarantees to efficiently obtain an optimum to the original optimal control problem via solving a sequence of simplified problems which gradually judge and reserve only the active collision-avoidance constraints. A first-regularization-then-action strategy, together with the look-up table technique, is developed for online solutions. At the regularization stage, the vehicles form a standard formation by linear acceleration/deceleration only. At the action stage, the vehicles execute lane change motions computed offline and recorded in the look-up table. This makes online motion planning feasible because 1) the computational complexity at the regularization stage scales linearly rather than exponentially with the vehicle number; and 2) online computation at the action stage is fully avoided through data extraction from the look-up table.

Duo Xiao

Papers

Centralized and optimal motion planning for large-scale AGV systems