Mixed traffic flow-oriented vehicle eco-driving control method and electronic device

A mixed traffic flow-oriented vehicle eco-driving control system, said mixed traffic flow-oriented vehicle eco-driving control system comprising:

• a central controller;
• and
• a plurality of local controllers deployed on the connected and autonomous vehicle;
• wherein the central controller comprises:
• a first result acquiring module, configured to
• acquire signal phase and timing information, historical vehicle state information and real-time vehicle state information;
• calculate the gathering wave speed and the dissipation wave speed at the intersection based on the shock wave evolution theory, and obtain the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
• a second result acquiring module, configured to predict the vehicle state and the time when the connected and autonomous vehicle passes through the stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
• a manual-driving vehicle trajectory predicting module, configured to construct a longitudinal acceleration prediction method of the manual-driving vehicle based on the risk field model, so as to acquire predicted trajectory of the manual-driving vehicle;
• a connected and autonomous vehicle trajectory planning module, configured to take the instantaneous fuel consumption of the connected and autonomous vehicle and the penalty for the state of passing through the stop line as the objective function, set dynamic constraints and trajectory constraints of the connected and autonomous vehicle, construct and solve the optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtain the acceleration curve of the connected and autonomous vehicle, so as to acquire the ecological reference trajectory of the connected and autonomous vehicle;
• and
• a first communication module, configured to send the ecological reference trajectory of the connected and autonomous vehicle to a local controller of the plurality of local controllers;
• wherein the local controller comprises:
• a second communication module, configured to receive the ecological reference trajectory of the connected and autonomous vehicle;
• a connected and autonomous vehicle tracking module, configured to set the risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, construct the tracking target based on the risk factor and the ecological reference trajectory, use the model predictive control for solving, and obtain the control input of the connected and autonomous vehicle,
• wherein the local controller is further configured to control, based on the control input of the connected and autonomous vehicle, the connected and autonomous vehicle for driving.

An electronic device comprising:

• a memory and a processor,
• wherein the memory is coupled with the processor,
• wherein the memory is configured to store program data, and
• wherein the processor is configured to execute the program data to implement a mixed traffic flow-oriented vehicle eco-driving control method,
• wherein the mixed traffic flow-oriented vehicle eco-driving control method comprises
• acquiring signal phase and timing information, historical vehicle state information and real-time vehicle state information;
• calculating a gathering wave speed and a dissipation wave speed at an intersection based on a shock wave evolution theory, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time when a farthest queue is formed;
• predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
• predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle;
• taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle;
• setting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle;
• and
• controlling, based on the control input of the connected and autonomous vehicle, the connected and autonomous vehicle for driving.