Preceding vehicle state prediction

• US 8,948,995 B2
• Filed: 06/28/2012
• Issued: 02/03/2015
• Est. Priority Date: 06/28/2012
• Status: Active Grant

• Current Assignee: Toyota Jidosha Kabushiki Kaisha (Toyota Motor Corporation)
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: Pandita, Rohit, Caveney, Derek Stanley
• Primary Examiner(s): Khatib, Rami
• Application Number:
  US13/535,513
• Publication Number:
  US 20140005906A1
• Time in Patent Office: 950 Days
• Field of Search: 701/1, 701/23, 701/24, 701/36, 701/96, 701/117, 701/118, 701/119, 701/482, 340/434, 340/435, 340/988, 180/168
• US Class Current: 701/96
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 30/17 with provision for special action when the preceding vehicle comes to a halt, e.g. stop and go
• CPC Class Codes: B60W 40/04 Traffic conditions

Abstract

A platoon model allows improved prediction of preceding vehicle future state. In this context, the preceding vehicle is a vehicle immediately ahead of the host vehicle, and the dynamic state of the preceding vehicle was predicted based on data received from one or more vehicles in the platoon. The intelligent driver model (IDM) was extended to model car-following dynamics within a platoon. A parameter estimation approach may be used to estimate the model parameters, for example to adapt to different driver types. An integrated approach including both state prediction and parameter estimation was highly effective.

Claims

1. A method of predicting a dynamic state of a preceding vehicle within a platoon, the preceding vehicle being immediately ahead of a host vehicle, the platoon also including a forward vehicle at a foremost position of a platoon, the method comprising:

providing a platoon model, the platoon model being a dynamic model of the platoon and having model parameters related to each vehicle in the platoon;

estimating model parameters for the platoon;

adaptively updating the model parameters using vehicle data received from vehicles within the platoon;

using the platoon model to predict the dynamic state of the preceding vehicle,

the platoon being a line of vehicles on a road, the platoon including the host vehicle, the preceding vehicle, and a forward vehicle ahead of the preceding vehicle, at the foremost position of the platoon,

the platoon model being derived from a car-following model for each leader-follower vehicle pair in the platoon,

the dynamic state including position and velocity of the preceding vehicle.

2. The method of claim 1, the car-following model being an intelligent driver model.

3. The method of claim 2, the model parameters for each vehicle including a maximum acceleration, a maximum deceleration, a target cruise velocity, a jam-distance, and a headway parameter.

4. The method of claim 3, target cruise velocity being determined from a known speed-limit for the road, or from historical traffic data.

5. The method of claim 3, target cruise velocity being determined from a cruise control setting for the host vehicle.

6. The method of claim 3, adaptively updating the model parameters;

updating only the headway parameter for each vehicle,

the headway parameter being headway time or headway distance.

7. The method of claim 1, the method further comprising:

computing a state-prediction error by comparing previously predicted states with current measurements;

executing a parameter estimation method to estimate new parameters for the platoon model, when the state-prediction error exceeds a numerical threshold.

8. The method of claim 7, parameter estimation being used to update only vehicle headway parameters.

9. The method of claim 7, updating the model parameter being performed aperiodically for adaptively updating the platoon model parameters.

10. The method of claim 1, vehicle data being received through wireless communication.

11. The method of claim 1, vehicle data being received from vehicles within the platoon through wireless vehicle-to-vehicle communication.

12. The method of claim 1, the dynamic state of the preceding vehicle being predicted at least 3 seconds in advance.

13. An apparatus, the apparatus being an adaptive cruise control for a host vehicle, the apparatus including:

a wireless receiver, operable to receive vehicle data from vehicles ahead of the host vehicle;

an electronic circuit, receiving the vehicle data and providing a vehicle control output to control a vehicle speed of the host vehicle,

the electronic circuit being operable to predict the dynamic state of a preceding vehicle immediately ahead of the host vehicle when the host vehicle is in traffic, the dynamic state being predicted using a platoon model,

the platoon model being a dynamic model having model parameters related to vehicles ahead of the host vehicle, the model parameters being estimated and then adaptively updated using the vehicle data,

the electronic circuit using the platoon model to predict the dynamic state of the preceding vehicle, and provide the vehicle control output to modify the speed of the host vehicle in response to the predicted dynamic state of the preceding vehicle,

the platoon model being derived from a car-following model for each leader-follower vehicle pair in the platoon, the dynamic parameters of the preceding vehicle including the speed of the preceding vehicle.