Deep reinforcement learning-based low-speed vehicle following decision-making method
A technology that reinforces learning and decision-making methods, applied in vehicle position/route/altitude control, motor vehicles, two-dimensional position/airway control, etc., can solve problems such as gaps, improve fidelity, improve driving comfort and traffic Effects of security, strong versatility and flexibility
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[0043] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:
[0044] The present invention provides a vehicle low-speed car-following decision-making method based on deep reinforcement learning. The vehicle low-speed car-following decision-making method based on deep reinforcement learning not only improves driving comfort, but also ensures traffic safety, and improves traffic jams. Flow rate
[0045] In this embodiment, as figure 1 The frame diagram shown shows the specific process of this embodiment:
[0046] Step 101: Receive the position, speed, and acceleration information of the front vehicle and the rear vehicle in real time through the Internet of Vehicles, as the environment state, express the current state and behavior of the unmanned vehicle, which specifically includes:
[0047] (1) The position, speed, and acceleration information of the three vehicles in front received in real time through the Interne...
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