Decision planning method for automatic driving of special vehicle

Abstract

The invention relates to a decision planning method for automatic driving of a special vehicle. The method comprises the steps of: 1) an automatic driving operation module obtaining the current position and pose of a vehicle; 2) projecting environment information sent by a perception system to a grid map, and generating an environment map; 3) the automatic driving operation module obtaining a control instruction of a current work executor and issuing the control instruction; 4) the automatic driving operation module obtaining a task reference route, performing trajectory cluster planning combined with vehicle dynamics constraints by a route-speed decomposed trajectory planning method, obtaining a basic trajectory cluster executable by the vehicle, and fusing the basic trajectory cluster and the task reference route to obtain an executable trajectory cluster; and 5) performing safety and high efficiency selection on the planned executable trajectory cluster, and finally generating a high-gain trajectory. According to the decision planning method for the automatic driving of the special vehicle, compared with the prior art, the method has the advantages of improving obstacle avoidance success rate, automatic decision, multi-mode trajectory decision strategy, achieving automatic driving safety and the like.

Description

technical field [0001] The invention relates to the field of vehicle trajectory planning, in particular to a decision-making and planning method for an automatic driving special operation vehicle. Background technique [0002] In recent years, the rapid development of artificial intelligence technology, the substantial improvement of computer hardware computing capabilities, the continuous improvement of perception systems, and the maturity of vehicle electrification and wire control have made it possible to implement autonomous driving technology. Unmanned passenger vehicles are currently the most intense application direction of autonomous driving technology, including Google Waymo, Baidu Apollo, etc. The more urgent demand for driving skills is not only for the consideration of driving safety, but equally important is the reduction of the driver's workload, the increase in labor demand and the alleviation of the contradiction between the shortage of skilled workers. Ther...

AI Technical Summary

Problems solved by technology

This will greatly reduce the operating efficiency of special operation vehicles

In addition, due to the measurement error caused by the FOV, resolution, and measurement accuracy of the perception system, the system error after rasterization, and the occlusion of obstacles, there are serious uncertainties in the environmental map passed to the decision-making plan. Considering the operation safety of special operation vehicles, planning decisions need to be compatible with these uncertainties

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