Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

Patent metadata

Field	Value
Publication number	US-11370435-B2
Application number	US-201916559738-A
Country	US
Kind code	B2
Filing date	Sep 4, 2019
Priority date	Sep 4, 2019
Publication date	Jun 28, 2022
Grant date	Jun 28, 2022

Abstract

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

First claim

What is claimed:

1. A method for controlling automated driving operations of a motor vehicle, the method comprising:
determining, via a vehicle controller of the motor vehicle, a vehicle origin and a vehicle destination for the motor vehicle;
conducting, via the vehicle controller using a memory-stored map database, a geospatial query to generate a graphical representation of a road network between the vehicle origin and the vehicle destination with multiple candidate routes for the motor vehicle;
receiving, via the vehicle controller, respective road-level data associated with each of the candidate routes, the road-level data including speed, turn angle, and/or gradient data;
estimating, via the vehicle controller based on the respective road-level data, a respective total energy use of the motor vehicle to traverse from the vehicle origin to the vehicle destination across each of the candidate routes;
determining, for the candidate route with a lowest of the estimated total energy uses, multiple candidate driving strategies each having respective speed and acceleration profiles;
selecting an optimal driving strategy from the candidate driving strategies through a cost evaluation of the associated speed and acceleration profiles and a forward simulation of movement of the motor vehicle and a lead vehicle over a calibrated prediction horizon, the optimal driving strategy corresponding to the candidate driving strategy having a multi-objective cost function with a lowest cost value, the multi-objective cost function including multiple cost metrics each calculated from a respective one of the speed and acceleration profiles and the forward simulation of the movement of the motor vehicle and the lead vehicle, wherein selecting the optimal driving strategy includes:
calculating, for each of the candidate driving strategies, respective occupant security, energy consumption, progress, and occupant comfort cost metrics based on the respective speed and acceleration profiles associated with the candidate driving strategy, and
calculating, for each of the candidate driving strategies, a respective total cost as a mathematical sum of the respective occupant security, energy consumption, progress, and occupant comfort cost metrics over a fixed time horizon; and
transmitting, via the vehicle controller, a command signal to a vehicle steering system and/or a powertrain of the motor vehicle to execute a control operation based on the optimal candidate driving strategy for the candidate route with the lowest estimated total energy use.

2. The method of claim 1 , wherein generating the graphical representation of the road network between the vehicle origin and the vehicle destination includes:
dissecting each of the candidate routes into multiple adjoining road segments; and
determining respective distance and time data for each of the road segments based on the received road-level data.

3. The method of claim 2 , wherein estimating the respective total energy use of the motor vehicle for each of the candidate routes includes determining segment-level energy uses by evaluating the respective road-level data of each of the corresponding road segments against a memory-stored table correlating energy consumption to speed, turn angle, and/or gradient.

4. The method of claim 3 , further comprising:
overlaying each of the segment-level energy uses with a respective graph edge representing the road segment in the graphical representation of the road network; and
determining the candidate route with the lowest of the estimated total energy uses by applying a Dijkstra algorithm to the graphical representation of the road network with the overlaid segment-level energy uses.

5. The method of claim 4 , further comprising assigning each of the graph edges with a respective weighted and synthesized cost of time, distance, and energy.

6. The method of claim 5 , wherein determining the distance and time data for each of the road segments includes synthesizing the respective distance and time data from static and dynamic map information with historical driving data for the corresponding road segment.

7. The method of claim 1 , further comprising monitoring vehicle status and road condition data at a fixed refresh rate, the vehicle status and road condition data including a host vehicle speed of the motor vehicle, a lead vehicle speed of the lead vehicle, a headway gap distance between the motor vehicle and the lead vehicle, a real-time speed limit, and a distance to a next stop sign and/or stop light.

8. The method of claim 1 , wherein determining the multiple candidate driving strategies includes:
dissecting each of the candidate routes into multiple adjoining road segments; and
cataloguing a time array of vehicle speeds for each of the road segments in a fixed time horizon as limited by a real-time speed limit and traffic data.

9. The method of claim 1 , wherein determining the multiple candidate driving strategies includes:
dissecting each of the candidate routes into multiple adjoining road segments;
determining a time array of estimated headway gap distances between the motor vehicle and the lead vehicle for each of the road segments;
predicting a lead vehicle speed profile of the lead vehicle for each of the road segments; and
determining a respective speed profile of the motor vehicle for each of the road segments in a fixed time horizon based on the time array of estimated headway gap distances and the predicted lead vehicle speed profiles as limited by a real-time speed limit and traffic data for the road segment.

10. The method of claim 1 , further comprising deriving the forward simulation of movement of the motor vehicle and the lead vehicle over the calibrated prediction horizon based on the candidate driving strategies of the motor vehicle, estimated lead vehicle speeds and accelerations of the lead vehicle, and estimated headway gap distances between the motor vehicle and the lead vehicle over a receding time horizon.

11. The method of claim 1 , further comprising processing the optimal driving strategy with a short-term path planner to refine and smooth the respective speed and acceleration profiles associated with the optimal driving strategy.

12. The method of claim 11 , further comprising generating a path reference data set and a speed reference data set for the processed optimal driving strategy through a trajectory reference extraction that decouples path data from the candidate route with the lowest estimated total energy use and decouples speed data from the respective speed and acceleration profiles of the optimal driving strategy.

13. The method of claim 12 , wherein the command signal includes:
an autonomous lateral steering control command derived from the path reference data set and transmitted to the vehicle steering system; and
an autonomous longitudinal speed and acceleration control command derived from the speed reference data set and transmitted to the vehicle powertrain.

14. The method of claim 1 , wherein estimating the respective total energy use for each of the candidate routes includes:
dissecting each of the candidate routes into multiple adjoining road segments;
determining a vehicle speed and a turning angle for each of the road segments;
determining a respective fuel usage for each of the road segments as a function of the vehicle speed and the turning angle of the road segment; and
aggregating the fuel usages for all of the road segments of the candidate route.

15. The method of claim 1 , wherein estimating the respective total energy use of the motor vehicle to traverse each of

Assignees

• Gm Global Tech Operations Llc
• Univ Carnegie Mellon

Inventors

• Zhao Junfeng
• Chang Chen-Fang
• Hegde Bharatkumar
• Chang Insu
• Rajkumar Ragunathan

Classifications

• B60W2554/00

  Input parameters relating to objects · CPC title

• G01C21/3469

  Fuel consumption; Energy use; Emission aspects · CPC title

• B60W2554/801

  Lateral distance · CPC title

• B60W2554/804

  Relative longitudinal speed · CPC title

• B60W30/18 Primary

  Propelling the vehicle · CPC title