Speed ​​control and trajectory optimization method of networked automatic vehicles based on expressway merge area

Abstract

The invention discloses a network-connected automatic vehicle speed control and trajectory optimization method based on the expressway merging area, judging whether there is a CAV entering the acceleration lane, and if so, starting to execute T 1 The control step: T 1 Judging T from the acquired data 1 Based on the speed of the front and rear vehicles, the distance between vehicles, and the distribution of acceleration, T 1 Meet the minimum speed requirement to enter the main road and draw a T 1 The acceleration trajectory map of the acceleration lane, and start trajectory optimization: T 1 Merge safely into the main road from the acceleration lane, draw a T 1 ~T 5 The trajectory map of the whole journey is T 1 Merge into the main road to provide visual trajectory display. The present invention is based on the speed control and trajectory optimization method of the networked automatic vehicle in the expressway merging area, which is oriented to the mixed traffic flow under the future trend, and the proposed speed control and trajectory optimization method is conducive to improving the stability of the mixed traffic flow in the expressway merging area and security.

Description

Technical field [0001] The present invention belongs to the field of CAV speed and trajectory control technology, which relates to a network-based automatic vehicle speed control and trajectory optimization method based on high-speed cross-flow region. Background technique [0002] In recent years, with the rapid development and extensive application of traffic infrastructure, the rapid development of intelligent transportation and wisdom is high-speed. Intelligent Net Environment and Car Road Collaboration Technology provide reliable system guarantee and technical support for the traffic system under the integration of human traffic. The "Traffic Strong National Division" proposes to push the development of intelligent transportation systems to new heights and new strategies. In traffic flow elements, it has gradually developed from "all artificial vehicles" to "some artificial vehicles + partially intelligent network vehicle + partial automatic vehicle" mixed traffic flow trend...

AI Technical Summary

Problems solved by technology

At the same time, because the speed control is affected by many factors (weather, driver characteristics, road characteristics), the real-time speed control and trajectory optimization methods are still lagging behind.

[0004] CAV-oriented speed control and trajectory planning still have the following deficiencies: (1) Existing studies are mostly carried out from full HVs or full CAVs, which makes the existing methods have a certain hysteresis. The research on mixed traffic flow integrated with CAV under the acceleration lane has high application value and practicality; (2) most of the existing speed control methods take traffic flow as the research object, that is, to control and evaluate the speed of macroscopic traffic flow, However, no effective research has been carried out on the speed control and trajectory optimization of a specific CAV when the acceleration lane merges into the main road.

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