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It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.

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Motion planning is an essential part of autonomous vehicles. The planning process should respond to environmental changes in real time to ensure safety. This paper proposes an event-triggered real-time motion planning strategy to achieve a more real-time planning effect and a scenario-based planning process. The path planning process is discretized into several parts and integrated into the behavioral planning process. A hierarchical finite state machine (HFSM) based integrated motion planning process is proposed to trigger the discrete path planning parts according to environmental events. Thus, the output reference path can be obtained. Experiment and simulation results show the efficiency of our strategy.

In order to control the grain size in thermomechanical processing, the grain growth behavior of hot extruded Mg–xAl–1Zn (x = 3, 6, 9) alloys and their relationship with second phase particles and solutes were investigated. The growth rate of AZ61 is greater than that of AZ31 and AZ91 at 300 °C, 350 °C, 400 °C, and 450 °C under isothermal annealing. The average grain growth exponents n of Mg–xAl–1Zn (x = 3, 6, 9) alloys were 2.26, 2.33, and 2.53 at 300–400 °C, respectively. The deviation from the theoretical value of 2 was attributed to the hindrance of grain boundary migration of Al-rich second phase particles and solute Al. Microscopic observations show that the grain size of the annealed samples is closely related to the shape, volume fraction, size, and distribution position of the second phase particles. Significantly, the pinning effect is stronger for lamellar and network-like second phase particles. In addition, the pinning effect of Al-rich second phase particles plays a more important role in grain refinement than the dragging of solute Al. The growth of abnormal grains in the microstructure is attributed to the high energy difference between the preferentially oriented <112¯0> grains and the surrounding grains, which drives the grain boundaries to overcome the same pinning force of the second phase particles.