Automatic driving vehicle trajectory planning method in mixed traffic flow environment

Abstract

The invention provides an automatic driving vehicle trajectory planning method for fixed signal timing in a manual and automatic driving mixed driving environment. The method comprises the following steps: acquiring information of a vehicle driving into an intersection control area; judging the type of the entering vehicle, if the entering vehicle is a manual driving vehicle, predicting the trajectory of the vehicle by adopting a Newell car-following model, and if the entering vehicle is an automatic driving vehicle, planning the trajectory of the automatic driving vehicle by adopting a segmented trajectory planning method; the track information of the vehicle is stored, when it is detected that the next vehicle is driven in, the current vehicle information is updated, and vehicle track planning is started. The method belongs to a heuristic algorithm, track planning of the automatic driving vehicle in the mixed driving environment is achieved, the planning efficiency is improved on the premise that safety is guaranteed, and vehicle track planning can be achieved for signalized intersections under different traffic flows, signal phases and vehicle permeability.

Description

technical field [0001] The invention relates to the technical field of road traffic control, in particular to a trajectory planning method for an automatic driving vehicle in a mixed traffic flow environment. Background technique [0002] As the core part of urban roads, signalized intersections are the key to improving the efficiency of urban road traffic, and have always been the focus of attention. Under the vehicle-road coordination system, by planning and guiding the trajectory of autonomous vehicles at the entrance of the intersection, the congestion at the signalized intersection can be effectively alleviated, thereby improving the overall traffic capacity of urban roads. [0003] In the mixed traffic flow environment of human-driven vehicles and self-driving vehicles, the self-driving vehicles are controllable vehicles. Facing signal-controlled intersections, the trajectory planning of autonomous vehicles requires vehicles to pass through the intersection as soon as...

AI Technical Summary

Problems solved by technology

However, such optimal vehicle control models generally require the establishment of complex nonlinear objective functions and constraints, which are difficult to solve and have low computational efficiency.

For vehicle trajectory planning, detailed acceleration adjustment can theoretically obtain the optimal driving trajectory under a certain target, but considering the actual control effect of the vehicle and the complexity of calculation, the optimal control model is not the best choice for vehicle trajectory control

In addition, although the traffic efficiency of road intersections can be improved by controlling self-driving vehicles, it will take a certain amount of time for the popularization of self-driving vehicles. In the future, the road traffic flow will be mixed with self-driving vehicles and human-driving vehicles. Appear

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