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Automatic vehicle following method and system for simulating driver characteristics on the basis of LSTM

An automatic car-following and self-car technology, which is applied to vehicle components, biological neural network models, external condition input parameters, etc. Data timing characteristics and other issues to achieve self-learning, smooth car-following control process, and self-adaptation

Active Publication Date: 2018-12-07
安徽科大擎天科技有限公司
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since the driver's car following action is a continuous process, the existing automatic car following systems basically use single-frame sensor data as the consideration factor of the control model, and do not consider the influence of the timing characteristics of the data, so the control system designed Can't simulate the driver's following behavior very well
At the same time, due to the great differences in driving styles, behavior trends and safety requirements among individual drivers, the existing automatic car-following systems basically adopt a relatively fixed control strategy. Difficult to guarantee adaptability to different drivers

Method used

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  • Automatic vehicle following method and system for simulating driver characteristics on the basis of LSTM
  • Automatic vehicle following method and system for simulating driver characteristics on the basis of LSTM
  • Automatic vehicle following method and system for simulating driver characteristics on the basis of LSTM

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Embodiment 1

[0039] Such as figure 1 As shown, the present embodiment provides an LSTM-based automatic car following method for simulating driver characteristics, which is used to control the speed of the target vehicle itself so as to maintain the distance between the target vehicle and the vehicle in front or keep the vehicle speed; including the following steps:

[0040]Step S101, establish a training sample library according to the driving training data collected in different places and weathers; the driving training data includes input feature data and corresponding driving manipulation data; the input feature data includes the speed of the vehicle in front, the speed of the vehicle in front, the Vehicle acceleration, inter-vehicle distance, the driving manipulation data includes accelerator pedal and brake pedal sensor data. In practical applications, experimental vehicles can be used to collect driving training data in real urban road environments. The above-mentioned empirical dat...

Embodiment 2

[0091] Such as image 3 As shown, an LSTM-based automatic car-following system for simulating driver characteristics includes an environment perception module 1, a training sample library module 2, an automatic car-following control algorithm module 3, and a lower computer execution module 4; wherein:

[0092] The environment perception module 1 includes: a vehicle speed sensor submodule 11, an acceleration sensor submodule 12 and a radar system submodule 13; The self-vehicle speed v1 of the target vehicle, the acceleration sensor submodule 12 (specifically, Xsens MTI-G-710 inertial element can be selected) is used to obtain the self-vehicle acceleration a of the target vehicle in real time, and the radar system submodule 13 (specifically can be IBEO LUX4L laser radar) is used to obtain the speed v2 of the vehicle in front and the distance s between the target vehicle and the vehicle in front in real time.

[0093] The training sample library module 2 is used to set up a trai...

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Abstract

The invention provides an automatic vehicle following method and system for simulating driver characteristics on the basis of LSTM. According to the automatic vehicle following method and system for simulating the driver characteristics on the basis of the LSTM, an LSTM recurrent neural network model is introduced; the model adopts sensor information time sequence data, automatic vehicle running time sequence data and the like, which are collected in the steady state vehicle following process of an excellent driver, so as to learn driver vehicle following driving behavior characteristics and establish a non-linear input and output mapping relation knowledge base; and therefore, longitudinal operation control of a vehicle in the vehicle following running process is predicated to realize automatic adaption of the system to the driver characteristics. According to the automatic vehicle following method and system for simulating the driver characteristics on the basis of the LSTM, the driver vehicle following behavior characteristics are simulated by using a characteristic that an LSTM recurrent neural network is good at processing time sequence characteristic data; the output of a designed controller conforms to driving behavior characteristics of a human being on the premise of meeting safety, accuracy and comfort; and meanwhile, self-learning of the driver operation process characteristics can be effectively realized; the self-adaption of the system to the driver characteristics is realized; and a generally applicable range is realized.

Description

technical field [0001] The invention belongs to the technical field of intelligent driving, and in particular relates to an LSTM-based automatic car-following method and system for simulating driver characteristics. Background technique [0002] Autonomous driving is divided into two stages, the advanced driver assistance stage and the automatic driving stage. The advanced driver assistance system is the basis of automatic driving. Although the development of autonomous driving is in full swing, most of the current mass-produced cars are equipped with advanced driver assistance systems. The automatic follow-up system is one of the important components of the adaptive cruise system, and it is also an essential function of the vehicle's advanced driver assistance system. [0003] Existing systems of this type output distance and relative movement information between the self-vehicle and the vehicle in front based on vision or radar sensing systems, and control the speed of the...

Claims

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Application Information

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IPC IPC(8): B60W30/165G06N3/04
CPCB60W30/165B60W2520/105B60W2520/10B60W2554/804B60W2554/801G06N3/045
Inventor 程腾曹聪聪杜卿宇蒋亚西
Owner 安徽科大擎天科技有限公司
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