Emergency vehicle hybrid lane changing decision-making method based on reinforcement learning and avoidance strategies

Abstract

The invention relates to an emergency vehicle hybrid lane changing decision-making method based on reinforcement learning and avoidance strategies. The method comprises the following steps: determining an optimized road section and execution strategies of ICCV and ICEV to be planned; initializing a DQN network of the ICEV to be planned; obtaining a state space of the DQN network based on the stateinformation of the ICEV to be planned, the six neighbor vehicles of the ICEV to be planned and the avoidance strategy execution condition of the preceding vehicle of the ICEV; obtaining an output value based on the state space of the DQN, and obtaining a preliminary decision and an action space based on the output value; establishing an action selection barrier, and verifying and selecting the obtained preliminary decision until the action finally selected from the output value or the action space meets the traffic rule and the road physical structure; defining a reward function for calculating a total reward corresponding to the action; and training the DQN network to obtain a trained DQN network. The method can be widely applied to the field of road lane changing decision control.

Description

technical field [0001] The invention belongs to the field of road lane-changing decision-making control, and in particular relates to a mixed lane-changing decision-making method for emergency vehicles based on reinforcement learning and an avoidance strategy. Background technique [0002] At present, most of the relevant research on reducing the response time of emergency vehicles focuses on route optimization and traffic light control, trying to solve the problem from the perspective of macro-scheduling, such as: Dijkstra algorithm, ant colony algorithm (ACA), A* and hybrid leapfrog algorithm (SFLA). However, these macro-level approaches do not take full advantage of real-time traffic data, seldom provide micro-level control of autonomous emergency vehicles, hardly consider the impact on normal traffic flow, and ignore the delay of response time on straight roads. [0003] In addition, a small number of studies have mentioned deterministic algorithms for micro-controlling...

AI Technical Summary

Problems solved by technology

However, these macro-level approaches do not take full advantage of real-time traffic data, seldom provide micro-level control of self-driving emergency vehicles, hardly consider the impact on normal traffic flow, and ignore the delay of response time on straight roads.

[0003] In addition, a small number of studies have mentioned deterministic algorithms for micro-controlling the automatic driving of emergency vehicles on straight roads, such as a series of car-following and lane-changing strategies, including some targeted avoidance strategies, but the strategies obtained by deep reinforcement learning They are more difficult to generalize to various traffic scenarios than them, and are not necessarily optimal in exploring faster speeds

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