INTELLIGENT VEHICLE CONTROL METHOD, APPARATUS, AND CONTROL SYSTEM

1 . A control method, wherein the control method comprises:

obtaining a driving style model and a target speed of a vehicle at a current moment;

determining a speed control instruction based on the driving style model by applying at least the target speed as an input of the driving style model; and

controlling the vehicle based on the speed control instruction.

2 . The control method according to claim 1 , wherein the vehicle comprises a driving style model library, the driving style model library comprises a plurality of driving style models for a driver to select, and each driving style model indicates a different driving habit.

3 . The control method according to claim 1 , further comprises:

obtaining a customized driving style model based on at least driving data of a driver by using a machine learning algorithm, wherein the customized driving style model matches a driving habit of the driver; and

adding the customized driving style model to a driving style model library of the vehicle.

4 . The control method according to claim 1 , further comprises:

obtaining a customized driving style model based on at least driving data of a driver by using a machine learning algorithm, wherein the customized driving style model matches a driving habit of the driver; and

adding the customized driving style model to a second driving style model library of a second vehicle of the driver other than the vehicle through a cloud data center.

5 . The control method according to claim 1 , further comprises:

determining that a driver adjusts a driving mode of the vehicle through at least one of steering wheel rotation, braking, or a human-computer interaction interface; and

collecting driving data of a driver of the vehicle.

6 . The control method according to claim 1 , further comprises:

calculating, based on the target speed and an actual speed, an acceleration at which the vehicle reaches the target speed; and

obtaining the speed control instruction by using the acceleration and the target speed as an input of the driving style model.

7 . The control method according to claim 1 , further comprises:

obtaining a second driving style model selected by a driver and a second target speed of the vehicle at a second current moment;

determining a second speed control instruction based on the second driving style model by applying at least the second target speed as an input of the second driving style model; and

controlling the vehicle based on the second speed control instruction.

8 . The control method according to claim 1 , further comprises:

determining a first accelerator opening degree and a first brake value according to an error feedback algorithm;

determining a second accelerator opening degree and a second brake value based on the driving style model by applying at least the target speed as the input of the driving style model;

obtaining a third accelerator opening degree through calculation based on the first accelerator opening degree and the second accelerator opening degree;

obtaining a third brake value through calculation based on the first brake value and the second brake value; and

controlling the vehicle based on the speed control instruction, wherein the speed control instruction comprises the third accelerator opening degree and the third brake value.

9 . The control method according to claim 1 , further comprises:

determining the target speed based on road condition information at the current moment, wherein the road condition information comprises one or more pieces of information provided by a map system, a positioning device, or a fusion system of the vehicle.

10 . The control method according to claim 1 , wherein the speed control instruction comprises an accelerator opening degree and a brake value, the accelerator opening degree is a parameter used to control a vehicle acceleration of the vehicle, and the brake value is a parameter used to control vehicle braking of the vehicle.

11 . A control apparatus, wherein the control apparatus comprises:

at least one processor; and

one or more memories coupled to the at least one processor and storing program instructions for execution by the at least one processor to:

obtain a driving style model and a target speed of a vehicle at a current moment;

determine a speed control instruction based on the driving style model by applying at least the target speed as an input of the driving style model; and

control the vehicle based on the speed control instruction.

12 . The control apparatus according to claim 11 , wherein the vehicle comprises a driving style model library, the driving style model library comprises a plurality of driving style models for a driver to select, and each driving style model indicates a different driving habit.

13 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

obtain a customized driving style model based on at least driving data of a driver by using a machine learning algorithm, wherein the customized driving style model matches a driving habit of the driver; and

add the customized driving style model to a driving style model library of the vehicle.

14 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

obtain a customized driving style model based on at least driving data of a driver by using a machine learning algorithm, wherein the customized driving style model matches a driving habit of the driver; and

add the customized driving style model to a second driving style model library of a second vehicle of the driver other than the vehicle through a cloud data center.

15 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

determine that a driver adjusts a driving mode of the vehicle through at least one of steering wheel rotation, braking, or a human-computer interaction interface; and

collect driving data of a driver of the vehicle.

16 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

calculate, based on the target speed and an actual speed, an acceleration at which the vehicle reaches the target speed; and

obtain the speed control instruction by using the acceleration and the target speed as an input of the driving style model.

17 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

obtain a second driving style model selected by a driver and a second target speed of the vehicle at a second current moment;

determine a second speed control instruction based on the second driving style model by applying at least the second target speed as an input of the second driving style model;

control the vehicle based on the second speed control instruction.

18 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

determining a first accelerator opening degree and a first brake value according to an error feedback algorithm;

determining a second accelerator opening degree and a second brake value based on the driving style model by applying at least the target speed as the input of the driving style model;

obtaining a third accelerator opening degree through calculation based on the first accelerator opening degree and the second accelerator opening degree;

obtaining a third brake value through calculation based on the first brake value and the second brake value; and

controlling the vehicle based on the speed control instruction, wherein the speed control instruction comprises the third accelerator opening degree and the third brake value.

19 . The control apparatus according to claim 11 , wherein the one or more memories store the program instructions for execution by the at least one processor to:

determine the target speed based on road condition information at the current moment, wherein the road condition information comprises one or more pieces of information provided by a map system, a positioning device, or a fusion system of the vehicle.

20 . A non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores program instructions for execution by at least one processor to:

obtain a driving style model and a target speed of a vehicle at a current moment;

determine a speed control instruction based on the driving style model by applying at least the target speed as an input of the driving style model; and

control the vehicle based on the speed control instruction.