Safety automatic driving lane changing track planning method based on improved LSTM neural network

Abstract

The invention provides a safety automatic driving lane changing track planning method based on an improved LSTM neural network. The safety automatic driving lane changing track planning method comprises the following steps: S1, calculating a lane changing track curve; S2, calculating a trajectory security constraint; S3, determining an optimal trajectory meeting the trajectory security constraintsin the S2; S4, putting the optimal trajectory and the original trajectory in S3 into an improved LSTM neural network for training; and S5, outputting a final lane changing track, thereby constructinga set of complete dynamic vehicle lane changing track planning model. According to the method, the safety track is generated by adopting the track algorithm considering the safety, and then the safety track and the original track are learned by using the improved LSTM neural network, so that the decided final lane changing track can be closer to the human riding experience on the premise of safety. Therefore, the service level of the planned optimal lane changing track is high, and it can be guaranteed that passengers have high comfort experience and efficiency experience.

Description

technical field [0001] The invention relates to a lane changing trajectory planning method for safe automatic driving, in particular to a lane changing trajectory planning method for safe automatic driving based on an improved LSTM neural network. Background technique [0002] In recent years, autonomous driving has received great attention worldwide, and it is considered to be an important technology to alleviate traffic congestion, reduce traffic accidents and environmental pollution. The present invention focuses on a key technology in automatic driving, the lane-changing trajectory planning technology. As one of the basic operations of a vehicle, lane changing plays an important role in the safe driving of the vehicle. In current traffic accidents, more than 30% of road accidents are caused by unreasonable lane-changing operations. Therefore, only by building a complete lane-changing trajectory planning model can the occurrence of autonomous driving traffic accidents b...

AI Technical Summary

Problems solved by technology

First, it is assumed that the speed of the surrounding vehicles does not change during the lane change process, which is inconsistent with the real driving environment

Second, the current models do not consider the real-time response of the lane-changing vehicles according to the changes of the surrounding vehicle status during the lane-changing process, and the dynamic adjustment of the speed in real time, so these models may fail in real traffic environments

Third, in terms of safety, the above research believes that as long as the lane change is completed, the lane-changing vehicle and the vehicle in the target lane do not collide, and there is no need to maintain a safe distance when an emergency occurs. This method is actually Can't really guarantee the safety of changing lanes

Although they were all about learning human lane-changing behavior, these studies did not take into account the reaction time of humans during driving, and in the process they did not consider the safety issues that will arise during lane-changing, whether it is automatic Driving or human driving, safety is the first issue to be guaranteed in the driving process

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